Les M Sztandera

• PhD
• Professor (Full) at Philadelphia University

Accurate grading of Prostate Cancer (PCa) is vital for effective treatment planning and prognosis. This study introduces an advanced framework for Gleason Grade (GG) classification, addressing challenges in accuracy, computational efficiency, and interpretability. Utilizing the SICAPv2 dataset, which contains annotated prostate biopsy Whole Slide I...

Fuzzy sets methodology to automatically generate knowledge base for informed decision making is proposed. As a proof of concept it has initially been applied to generate regulatory/health/environmental guidance rules for textile and apparel companies. Subsequently, the system will be augmented to incorporate additional consumer goods, and down the...

Taking advantage of techniques from the field of Computational Intelligence, the goal of our research is to construct systems that can computationally design polymer optical fiber formulations with specified desirable consumer characteristics and to develop computational tools which can be used to rationalize and predict properties of polymeric mat...

Engineered fabrics are desired for military protective clothing applications. Such fabrics, exhibiting high tactile comfort, can be computationally designed. Through the use of an extensive database that contains handfeel, mechanical, construction, and tactile comfort data for fabrics, desired comfort can be predicted by measuring a limited number...

Fuzzy logic is applied to the category discrimination problem related to identification of mammary lesions as benign or malignant. Results of other similar studies are reviewed. The current analysis expands the fuzzy logic approach by using the normal distribution function as set membership functions and using a genetic algorithm to optimize perfor...

This paper addresses the use of a data analysis approach to extract information from a large number of failure equipment notifications. Based on that, a fuzzy system, capable of learning and optimizing the knowledge from historical evidence, is formed. Subsequently, its use as a guiding tool in decision making processes at the strategic level (esti...

This paper addresses the use of a data analysis approach to extract information from a large number of failure equipment notifications. Based on that, a fuzzy system, capable of learning and optimizing the knowledge from historical evidence, is formed. Subsequently, its use as a guiding tool in decision making processes at the strategic level (esti...

This paper reports on student-centered learning, dubbed Nexus Learning, and global collaborative approaches supported by technological advancements. It covers pedagogical issues related to these developments. It focuses on the incorporation of students with different social, cultural, and academic backgrounds into an industrial project situation wh...

The aim of this research is to use a data analysis/mining approach to extract information from a large number of failure equipment notifications, form a fuzzy system that would be capable of learning and optimizing the knowledge from historical evidence, and subsequently use it as a guiding tool in decision making processes.

Purpose – The second of a two-part series, this paper aims to explain the design and development of a hybrid system for reverse engineering. Design/methodology/approach – A prediction engine to map the perception of tactile sensations using a neural network engine was developed. Since seventeen mechanical properties form the input - and tactile com...

Purpose The second of a two‐part series, this paper aims to explain the design and development of a hybrid system for reverse engineering. Design/methodology/approach A prediction engine to map the perception of tactile sensations using a neural network engine was developed. Since seventeen mechanical properties form the input ‐ and tactile compfo...

Engineered fabrics are being used increasingly in commercial and domain-specific systems. Such fabrics with specified consumer-desired characteristics can be computationally designed. Through the use of an extensive database that correlates sensory and mechanical properties with tactile comfort assessments, desired comfort can be predicted by measu...

In this paper we explore complex relationships between mechanical and sensory properties of fabrics, and the perceived tactile comfort. Mechanical properties, measured objectively by Kawabata Evaluation System for Fabrics (KES-FB), and handfeel properties, measured subjectively by sensory expert panel, are related to the tactile comfort of fabrics...

Engineered fabrics are being used increasingly in commercial and domain-specific systems. Such fabrics with specified consumer-desired characteristics can be computationally designed. Through the use of an extensive database that correlates sensory and mechanical properties with tactile comfort assessments, desired comfort can be predicted by measu...

Taking advantage of techniques from the field of Artificial Intelligence, the goal of our research is to construct systems that can computationally design polymer optical fiber formulations with specified desirable consumer characteristics. Polymers can offer cost effective and easily handled and installed optical components provided that materials...

Polymeric materials are finding increasing application in commercial optical communication systems. Taking advantage of techniques from the field of artificial intelligence, the goal of our research is to construct systems that can computationally design polymer formulations, including polymer optical fibers, with specified desirable consumer chara...

Polymeric materials are finding increasing application in commercial optical communication systems. Taking advantage of techniques from the field of artificial intelligence, the goal of our research is to construct systems that can computationally design polymer formulations, including polymer optical fibers, with specified desirable consumer chara...

In this paper we explore complex relationships between mechanical and sensory properties of fabrics, and the perceived tactile comfort. Mechanical properties, measured objectively by Kawabata Evaluation System for Fabrics (KES-FB), and handfeel properties, measured subjectively by sensory expert panel, are related to the tactile comfort of fabrics...

The relationship between mechanical and sensorial properties of textile materials, with perceived comfort is complex and non-linear. A system to analyze the aforementioned relationship using Artificial Intelligence (AI) tools is developed. Since the number of parameters that influence perceived sensation is beyond the capability of any existing mod...

Polymer fibers are finding increasing applications in commercial optical communication systems. Polymer optical fibers, with specified desirable consumer characteristics, can be computationally designed. Through the use of an extensive structure - property correlation database, properties of polymers can be predicted by a Neural Network. In this pa...

Education for students preparing to enter corporate information technology departments will need to deal with a vast array technologies that are becoming commoditised. This paper outlines the fundamental concepts. It further presents the need for dynamic case models that can present real-world problem-solving concepts that are not only technical bu...

Polymer fibers are finding increasing applications in commercial optical communication systems. Polymer optical fibers, with specified desirable consumer characteristics, can be computationally designed. Through the use of an extensive structure-property correlation database, properties of polymers can be predicted by a Neural Network, and the stru...

A crucial task in polymer chemistry is the formulation of materials which satisfy strict property constraints. This paper describes the use of dynamic adaptive agents in the design of novel polymers. A neural network is used to predict the properties of a proposed polymer from its composition, while a genetic algorithm solves the inverse problem by...

A nonlinear model and a linear model have been developed to correlate glass transition temperature (Tg) and repeating units of polymers using a neural network and multiple linear regression analysis respectively. A set of descriptors, chosen based on previous studies of the relations between T g and polymer structure, was used to describe the struc...

Land use planning, particularly in suburban areas, is a specialized spatial resource allocation problem. Spatial relationships between regions in an image play an important role in scene understanding. When the objects in a scene are represented by crisp sets, the all-or-nothing definitions of the subsets actually add to the problem of generating r...

Land use planning, particularly in suburban areas, is a specialized spatial resource allocation problem. Structural information derived from hierarchical image objects plays an important role in land use classification. Hierarchically formed image objects are useful tools for image analysis and spatial modeling. Spatial relationships between region...

Quantitative structure-activity relationships (QSARs) are developed that correlate the observed mutagenic activity of 181 aromatic amine derivatives with a variety of molecular descriptors calculated using quantum-chemical semiempirical methodology. Conventional multiple linear regression techniques using five descriptors give a relationship that a...

In this research, forecasting models were built based on both univariate and multivariate analysis. Models built on multivariate fuzzy logic analysis were better in comparison to those built on other models. The performance of the models was tested by comparing one of the goodness-of-fit statistics, R2, and also by comparing actual sales with the f...

In this research, forecasting models were built based on both univariate and multivariate analysis. Models built on multivariate fuzzy logic analysis were better in comparison to those built on other models. The performance of the models was tested by comparing one of the goodness-of-fit statistics, R 2 , and also by comparing actual sales with the...

Quantitative structure–activity/property–activity relationships are developed that correlate the observed mutagenic behavior of 62 aminoazo derivatives and 12 of their reductive cleavage products with a variety of molecular descriptors calculated using quantum-chemical semiempirical methodology. Multilinear regression techniques using 8 descriptors...

Both statistical and computational intelligence approaches (neural networks and genetic algorithms) to apparel sales forecasting are considered. Sales for the year 2000 were forecast using sales data for 1997-1999). An average correlation of 90% was found between forecast and actual sales using either statistical time series analysis or genetic alg...

This research focuses on the use of soft computing to aid in the development of novel, state-of-the-art, nontoxic dyes which are of commercial importance to the U.S. textile industry. Where appropriate, modern molecular orbital (MO) and density functional (DF) techniques are employed to establish the necessary databases of molecular properties to b...

Traditionally, statistical time series methods like moving average (MA), auto-regression (AR), or combinations of them are used for forecasting sales. Since these models predict future sales only on the basis of previous sales, they fail in an environment where the sales are more influenced by exogenous variables such as size, price, color, climati...

In this paper, we are investigating both statistical and soft computing (e.g., neural networks) forecasting approaches. Using sales data from 1997 – 1999 to train our model, we forecasted sales for the year 2000. We found an average correlation of 90% between forecast and actual sales using statistical time series analysis, but only 70% correlation...

This research focuses on the use of soft computing to aid in the development of novel, state-of-the-art, non-toxic dyes which are of commercial importance to the U.S. textile industry. Where appropriate, modern molecular orbital (MO) and density functional (DF) techniques are employed to establish the necessary databases of molecular properties to...

This research focuses on the use of softt computing to aid in the development of novel, state-of-the-art, non-toxic dyes which are of commercial importance to the U.S. textile industry. Where appropriate, modern molecular orbital (MO) and density functional (DF) techniques are employed to establish the necessary databases of molecular properties to...

This book brings together original work from a number of authors who have made significant contributions to the evolution and use of nonstandard computing methods in chemistry and pharmaceutical industry. The contributions to this book cover a wide range of applications of Soft Computing to the chemical domain. Soft Computing applications are able...

Textiles and computing have long been associated. High volume and low profit margins of textile products have driven the industry to invest in high technology, particularly in the area of data interpretation and analysis. Thus, it is virtually inevitable that soft computing has found a home in the textile industry. Contained in this volume are six...

In this study, we designed an intelligent system to examine the influence of the demographic variables of gender, blood type, and race on the distribution of skin staphylococci bacteria on neonates up to 48 hours old. Bacterial samples were obtained from the axilla and groin of 200 babies, born over an 18-month period, and the staphylococci therein...

Spatial relationships between regions in an image play an important role in scene understanding. Humans are able to quickly ascertain the relationship between two objects, for example "B is to the right of A," or "B is in front of A," but this has turned out to be a somewhat illusive task for automation. When the objects in a scene are represented...

Practical successes have been achieved with neural network models in a variety of domains, including energy-related industry. The large, complex design space of electrical power systems (EPS) is only minimally explored in current practice. The satisfactory results that nevertheless have been obtained testify that neural networks are a robust modeli...

It is expected that the use of soft computing will increase greatly in industrial applications, because the conceptual structure of hard computing is much too precise in relation to the great imprecision of the world around us. This book aims at attracting researchers and engineers both in the fields of industrial electronics (IE) and computational...

Geometric properties and relations play central roles in the description and processing of spatial data. The properties and relations studied by mathematicians usually have precise definitions, but verbal descriptions often involve imprecisely defined concepts such as elongatedness or proximity. The methods used in soft computing provide a framewor...

The initial diagnosis of bacterial infections in the absence of laboratory microbiological data requires physicians to use clinical algorithms based on symptoms, patient history and infection site. Optimization of such algorithms would be achieved by including as many variables associated with bacterial infection as possible. Demographic data are e...

Previous studies have suggested that the demographic variables of age and blood type may serve as "risk factors" for infection by specific bacterial species. Since both demographic variables and bacterial species are defined using generally accepted parameters, they constitute highly suitable variables for the generation of a fuzzy logic program. A...

In this study, we designed a fuzzy logic system to examine the influence of the demographic variables of age, blood type, gender and race on bacterial infection rates using a medical database assembled over 17 months from patients presenting to Albert Einstein Medical Center. The intelligent system was created using 155 patients, randomly selected...

The structural and electronic properties of the positional isomers of monomethoxy-4-aminoazobenzene (n-OMe-AAB) have been investigated using density functional theory with a basis set that includes polarization functions on all the atoms. These azo dyes are of interest because their carcinogenic activities depend dramatically on the position (n) of...

Practical successes have been achieved with neural network models in a variety of domains, including energy-related industry. The large, complex design space of electrical power systems (EPS) is only minimally explored in current practice. The satisfactory results that nevertheless have been obtained testify that neural networks are a robust modeli...

Initial treatment of bacterial infections currently relies on the use of "best guess" hypotheses rather than pinpointing the organism responsible. In addition, the physician usually changes the antibiotics, once the microbiology analyses become available; a practice that has contributed to the growing problem of resistance. Several demographic risk...

This research involves the integration of fuzzy entropies (used in the context of measuring uncertainty and information) with computational neural networks. An algorithm for the creation and manipulation of fuzzy entropies, extracted by a neural network from a data set, is designed and implemented. The neural network is used to find patterns in te...

Groups of factories in which many units share a single site are common within the chemical industry. This clustering of a number of synthetic units leads to economies of scale through the sharing of resources, and minimization of direct costs such as those arising from the storage and transportation of chemicals. Among the resources usually shared...

The paper introduces ontogenic Fuzzy-CID3 algorithm (F-CID3) which combines a neural network algorithm and fuzzy sets into a single hybrid algorithm which generates its own topology. Two new methods, one based on a concept of a neural fuzzy number tree, and a class separation method are introduced in the paper and utilized in the algorithm. The F-C...

Knowledge-based neural networks are concerned with the use of numerical information, which forms the domain knowledge, obtained from sensor measurements to determine the initial structure of a neural network. Research on combining symbolic inductive learning with neural networks, as well as research on combining fuzzy logic with neural networks, is...

In this paper a method of fuzzy decision making applied to diagnosis of coronary artery stenosis is presented. The method uses a neural network approach for the diagnosis of stenosis in the three main coronary arteries (left anterior descending, right coronary artery, and circumflex). First, the knowledge base domain, 201Tl scintigram training data...

The paper introduces fuzzy neural trees and an approach for converting these trees into feedforward neural network architectures. The proposed approach is unique in that it introduces the ways to use either technology as a “tool” within the framework of a model based on the other. It is of the highest significance as it results in new neural networ...

Ordering fuzzy subsets is an important event in dealing with fuzzy decision problems in many areas. This issue has been of concern for many researchers over the years. Also, in the last several years, there has been a large and energetic upswing in neuroengineering research aimed at synthesizing fuzzy logic with computational neural networks. The t...

A concept of an automatic knowledge base tuning in complex systems is outlined. In real life systems although the system is successfully controlled by a human expert, some information may be lost in translating the expert's knowledge to linguistic rules. On the other hand, the information gathered by sensor measurements from past experiences is not...

A concept of a fuzzy control system that adjusts itself upon the error propagation between the real system and the model is introduced. Different possible error outcomes are defined, namely, right (left) error, right (left) missed error, rule lost error, symmetry lost error, right (left) shift error, and nonresponsive error. An application to the t...

Ranking fuzzy subsets is an important event in dealing with fuzzy decision problems in many areas, such as management sciences, engineering, and even social sciences. This issue has been of concern for many researchers over the years. Some twenty eight methods have been proposed in the publications for ranking fuzzy subsets. It is the purpose of th...

The authors' approach involved taking a set of "crisp" decision rules generated by a machine learning algorithm and converting them into fuzzy rules. These fuzzy rules utilized the previously specified 30 fuzzy sets. Then, fuzzy sets were derived to represent major coronary artery stenosis, using generalized fuzzy operators. Four such fuzzy sets we...

In this work the authors use fuzzy sets theory to evaluate and predict flexural strength and density of NASA 6Y silicon nitride ceramic. Processing variables of milling time, sintering time, and sintering nitrogen pressure are used as an input to the fuzzy system. Flexural strength and density are the output parameters of the system. Data from 273...

In this work, we utilize fuzzy sets theory to evaluate and make predictions of flexural strength and density of NASA 6Y silicon nitride ceramic. Processing variables of milling time, sintering time, and sintering nitrogen pressure are used as an input to the fuzzy system. Flexural strength and density are the output parameters of the system. Data f...

As the use of fuzzy set theory continues to grow, there is an increased need for methodologies and formalisms to manipulate obtained fuzzy subsets. Concepts involving relative position of fuzzy patterns are acknowledged as being of high importance in many areas. In this paper, we present an approach based on the concept of dominance in fuzzy set th...

Fuzzy entropy measures are used to obtain a quick convergence of a continuous ID3 (CID3) algorithm proposed by K.J. Cios and N. Liu (1991), which allows for self-generation of a hierarchical feedforward neural network architecture by converting decision trees into hidden layers of a neural network. To demonstrate the learning capacity of the fuzzy...

As the use of fuzzy set theory in computer vision continues to grow, there is an increased need for methodologies and formalisms to extract and manipulate this type of uncertainty from a digital image. The concepts involved in spatial relationships among image objects and regions have been investigated and acknowledged as being vague and ambiguous....

Practical successes have been achieved with neural network models in a variety of domains, including energy-related industry. The large, complex design space of electrical power systems (EPS) is only minimally explored in current practice. The satisfactory results that nevertheless have been ob tained testify that neural networks are a robust model...

In this research, forecasting models were built based on both univariate and multivariate analysis. Models built on multivariate fuzzy logic analysis were better in comparison to those built on other models. The performance of the models was tested by comparing one of the goodness-of-fit statistics, R2, and also by comparing actual sales with the f...

This research focuses on the use of soft computing to aid in the development of novel, state-of-the-art, non-toxic dyes that are of commercial importance to the U.S. textile industry. Where appropriate, modern molecular orbital (MO) and density functional (DF) techniques are employed to establish the necessary databases of molecular properties to b...

Typescript. Thesis (M.S.)--University of Missouri-Columbia, 1990. Includes bibliographical references (leaves 115-122). Microfilm.