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How to provide cost-effective strategies for Software Testing has been one of the research focuses in Software Engineering for a long time. Many researchers in Software Engineering have addressed the effectiveness and quality metric of Software Testing, and many interesting results have been obtained. However, one issue of paramount importance in software testing — the intrinsic imprecise and uncertain relationships within testing metrics — is left unaddressed. To this end, a new quality and effectiveness measurement based on fuzzy logic is proposed. Related issues like the software quality features and fuzzy reasoning for test project similarity measurement are discussed, which can deal with quality and effectiveness consistency between different test projects. Experiments were conducted to verify the proposed measurement using real data from actual software testing projects. Experimental results show that the proposed fuzzy logic based metrics is effective and efficient to measure and evaluate the quality and effectiveness of test projects.

Diabetes mellitus is a common chronic disease in recent years. According to the World Health Organization, the estimated number of diabetic patients will increase 56% in Asia from the year 2010 to 2025, where the number of anti-diabetic drugs that doctors are able to utilize also increase as the development of pharmaceutical drugs. In this paper, we present a recommendation system for anti-diabetic drugs selection based on fuzzy reasoning and ontology techniques, where fuzzy rules are used to represent knowledge to infer the usability of the classes of anti-diabetic drugs based on fuzzy reasoning techniques. We adopt the "Medical Guidelines for Clinical Practice for the Management of Diabetes Mellitus" provided by the American Association of Clinical Endocrinologists to build the ontology knowledge base. The experimental results show that the proposed anti-diabetic drugs recommendation system gets the same accuracy rate as the one of Chen et al.'s method (R. C. Chen, Y. H. Huang, C. T. Bau and S. M. Chen, Expert Syst. Appl.39(4) (2012) 3995–4006.) and it is better than Chen et al.'s method (R. C. Chen, Y. H. Huang, C. T. Bau and S. M. Chen, Expert Syst. Appl.39(4) (2012) 3995–4006.) due to the fact that it can deal with the semantic degrees of patients' tests and can provide different recommend levels of anti-diabetic drugs. It provides us with a useful way for anti-diabetic drugs selection based on fuzzy reasoning and ontology techniques.

The rapid development of modern society and continuous urbanization have resulted in a proliferation of functional buildings, which offer significant convenience to individuals, but pose significant fire hazards as well. How to detect the fire at the early stage is always the focus of research. This paper proposes a multi-information source fusion fire recognition method based on particle swarm optimization (PSO)-backpropagation (BP) neural networks and ResNet50. The PSO algorithm is applied to optimize the initial parameters of a BP neural network model, while data from three sensors — temperature, humidity and smoke — are integrated, through iterative training of the system, accurate recognition of sensor data can be achieved. Additionally, a method is proposed for the recognition of infrared fire images using ResNet50 and transfer learning. By improving the ResNet50 network model and migrating the ResNet50 pre-trained network weight, infrared fire image recognition accuracy is further enhanced. Then the sensor information recognition results and image information recognition results are input into the fuzzy system for fusion reasoning again, and the final decision is output according to the set fuzzy rules. Experimental findings demonstrate that the multi-information source fusion approach utilizing the PSO-BP neural network and ResNet50 significantly enhances the accuracy and response time of fire recognition, and achieves a remarkable recognition effect.

Many attributes contribute to product failures that result in warranty claims. In particular, there are situations where several attributes are used together as criteria for judging the warranty eligibility of a failed product. For example, automobiles warranty coverage has both age and mileage limits. The warranty policy characterized by a region in a two-dimensional plane with one axis representing product age and the other axis representing product usage is known as the "two-attribute" warranty policy. A number of procedures have been developed for analyzing the two-dimensional warranty policy. These procedures use many crisp data obtained from strictly controlled reliability tests. However, in real situations, these requirements might not be fulfilled. In extreme cases, the warranty claims data come from users whose reports are expressed in a vague way. This may be due to subjective and imprecise perception of failures by a user, imprecise warranty data record, or imprecise rate of usage record. This paper suggests fuzziness as an alternative to randomness for describing the two-dimensional warranty uncertainty. A new sets-as-points geometric view of fuzzy warranty sets is developed in this study. This view can reduce many errors of estimation and prediction of the cost associated with a variety of warranty policies including the "two-attribute" warranties and some reliability improvement warranties.

In this paper we describe how fuzzy reasoning can assist an automated program understanding/fault localization tool with program understanding tasks. We are developing such a tool called BUG-DOCTOR, which is based on a blackboard framework. A fuzzy reasoner is proposed as a component for one of its knowledge sources, the Plan Processor. The Plan Processor retrieves a set of program plans from a plan library using indices called signatures. These plans are candidates for matching against the code we are trying to understand. The fuzzy reasoner will support the Plan Processor with the task of ranking the retrieved plans in order of similarity to the target code. The most highly ranked plan can then be used for the complex plan/code matching required for automated program understanding. Experiments with a fuzzy reasoning prototype are promising, and we believe that this approach to plan processing may eliminate the need for exhaustive plan library searches. The success of the fuzzy reasoning approach could lead to automated program understanders that scale up for use on large software systems from a variety of problem domains.

This paper proposes a new reasoning technique on fuzzy production systems while given input knowledge is incomplete. Based on the fuzzy Petri net formalism, the proposed algorithm can infer all possible conclusions and their corresponding missing inputs. The most possible conclusion can also be determined based on the criteria of the minimum number of missing inputs as well as the degree of truth of the conclusion. In addition, finiteness and computational complexity of the algorithm is investigated. As real decisions are typically made under incomplete input knowledge, this reasoning technique provides more realistic applications for fuzzy production systems.

The focus of this paper is on an attempt towards a unified formalism to manage both symbolic and numerical information based on high-level fuzzy Petri nets (HLFPN). Fuzzy functions, fuzzy reasoning, and fuzzy neural networks are integrated in HLFPN In HLFPN model, a fuzzy place carries information to describe the fuzzy variable and the fuzzy set of a fuzzy condition. An arc is labeled with a fuzzy weight to represent the strength of connection between places and transitions. A fuzzy set and a fuzzy truth-value are attached to an uncertain fuzzy token to model imprecision and uncertainty. We have identified six types of uncertain transition: calculation transitions to compute functions with uncertain fuzzy inputs; inference transitions to perform fuzzy reasoning; neuron transitions to execute computations in neural networks; duplication transitions to duplicate an uncertain fuzzy token to several tokens carrying the same fuzzy sets and fuzzy truth values; aggregation transitions to combine several uncertain fuzzy tokens with the same fuzzy variable; and aggregation-duplication transitions to amalgamate aggregation transitions and duplication transitions. To guide the computation inside the HLFPN, an algorithm is developed and an example is used to illustrate the proposed approach.

This paper develops a hybrid model which provides a unified framework for the following four kinds of reasoning: 1) Zadeh's fuzzy approximate reasoning; 2) truth-qualification uncertain reasoning with respect to fuzzy propositions; 3) fuzzy default reasoning (proposed, in this paper, as an extension of Reiter's default reasoning); and 4) truth-qualification uncertain default reasoning associated with fuzzy statements (developed in this paper to enrich fuzzy default reasoning with uncertain information). Our hybrid model has the following characteristics: 1) basic uncertainty is estimated in terms of words or phrases in natural language and basic propositions are fuzzy; 2) uncertainty, linguistically expressed, can be handled in default reasoning; and 3) the four kinds of reasoning models mentioned above and their combination models will be the special cases of our hybrid model. Moreover, our model allows the reasoning to be performed in the case in which the information is fuzzy, uncertain and partial. More importantly, the problems of sharing the information among heterogeneous fuzzy, uncertain and default reasoning models can be solved efficiently by using our model. Given this, our framework can be used as a basis for information sharing and exchange in knowledge-based multi-agent systems for practical applications such as automated group negotiations. Actually, to build such a foundation is the motivation of this paper.

Human knowledge about monitoring process variables is usually incomplete. To deal with this partial knowledge many types of representation other than the quantitative one are used to describe process variables (qualitative, symbolic interval). Thus, the development of automatic reasoning mechanisms about the process is faced with this problem of multiple data representations. In this paper, a unified principle for reasoning about heterogeneous data is introduced. This principle is based on a simultaneous mapping of data from initially heterogeneous spaces into only one homogeneous space based on a relative measure using appropriate characteristic functions. Once the heterogeneous data are represented in a unified space, a single processing for various analysis purposes can be performed using simple reasoning mechanisms. An application of this principle within a fuzzy logic framework is performed here to demonstrate its effectiveness. We show that simple fuzzy reasoning mechanisms can be used to reason in a unified way about heterogeneous data in three well known machine learning problems.

The article proposes an adaptive, noise-exclusive and assessment-based fuzzy switching median filter for high intensity impulse noise. The filter adaptively changes the size of sliding window in accordance with noise intensity and noise-exclusive operation is performed only on noise-free pixels. These steps thus preserve the image details effectively. Judicious assessment of the previously restored pixels is made in order to use them in replacing the noisy pixels and further improve the efficiency of filter. With these possibilities, fuzzy switching median filter-based approach is also incorporated for improving the performance. Experimental results justified the efficacy of the proposed fuzzy-based filtering method qualitatively and quantitatively for twelve different highly corrupted noisy images, and showed significant improvement to existing algorithms.

This paper presents a selection of alternative approaches to handle classification problems using the paradigm of case-based reasoning with fuzzy concepts. Our main concern is classification and pattern recognition queries in a fuzzy environment. An example was developed to explain the various methods and the results compared.

A knowledge-based controller(KBC) used in process control systems is presented. It has three features: first, it does not need the mathematical model; secondly, the adjustable parameters of KBC have practical meanings, so they can be determined easily using human experience, and thirdly, the contribution of KBC to a controlled plant is separated into two parts: steady states contribution and transient contribution. A simple fuzzy reasoning is employed to tune the KBC parameters. The experimental and simulation results show that KBC is very effective especially when there are variations in the process dynamics.

People use natural languages to think, to reason, to deduce conclusions, and to make decisions. Fuzzy set theory introduced by L. A. Zadeh has been intensively developed and founded a computational foundation for modeling human reasoning processs. The contribution of this theory both in the theoretical and the applied aspects is well recognized. However, the traditional fuzzy set theory cannot handle linguistic terms directly. In our approach, we have constructed algebraic structures to model linguistic domains, and developed a method of linguistic reasoning, which directly manipulates linguistic terms, In particular, our approach can be applied to fuzzy control problems.

In many applications of expert systems or fuzzy control, there exist numerous fuzzy reasoning methods. Intuitively, the effectiveness of each method depends on how well this method satisfies the following criterion: the similarity degree between the conclusion (the output) of the method and the consequence of an if-then sentence (in the given fuzzy model) should be the "same" as that between the input of the method and the antecedent of this if-then sentence. To formalize this idea, we introduce a "measure function" to measure the similarity between linguistic terms in a domain of any linguistic variable and to build approximate reasoning methods. The resulting comparison between our method and some other methods shows that our method is simple and more effective.

The fuzzy systems based on the universal triple I method are investigated, and then their response functions are analyzed. First, the conclusions show that 100 fuzzy systems via the universal triple I method are approximately interpolation functions, which can be used in practical systems, and that 90 ones are approximately fitted functions, which may be usable. Second, as its special cases, the Compositional Rule of Inference (CRI) method and the triple I method are discussed, with the results that 19 fuzzy systems via the CRI method and 2 ones via the triple I method are practicable. Therefore, the universal triple I method has larger effective choosing space, which can obtain more usable fuzzy systems than the others. Lastly, it is found that the first implication and second implication, respectively, embody the function of rule base and reasoning mechanism, further demonstrating the reasonability of the universal triple I method.

The core of artificial intelligence (AI) research is intelligent information processing, and fuzzy reasoning and fuzzy neural networks are significant research areas in models of intelligent information processing. The study examines the robustness of fuzzy reasoning by using the property characteristics of triangular modal operators and implication operators, which are used to analyse the Lipschitz property of fuzzy operators and its corresponding property characteristics of triangular modal operators and implication operators. Inference operators are used to investigate the robustness and error-control capabilities of fuzzy operators for fuzzy reasoning. The results showed that the robustness of quasi-copula fuzzy reasoning with fusion rules is better, and the corresponding maximum output perturbation is lowest at 0.36 when the fuzzy operator is T_M&R_MTc and the corresponding maximum output error is lowest at 0.41 when the fuzzy operators are T_M&R_MKl and T_M&R_Ma. This shows the optimal performance of the fuzzy associative memory model with fuzzy rule for quasi-copula fuzzy operators and can improve the theoretical technology. The best-performance fuzzy copula operator of the fuzzy associative memory model can enhance the theoretical foundation and technical support for the processing of intelligent information.

Forecasting the future is an important aspect of strategic planning. In recent years, an increasing number of corporate planners and forecasters have been turning to forecasting techniques to assist them in management decision-making. This paper presents a fuzzy management decision support system for scenario analysis. The system, developed in dBFAST, comprises two modules: data input and scenario generation. It is aimed at emulating the expert’s reasoning process in forecasting. It adopts a hybrid technique—a combination of fuzzy Delphi analysis and fuzzy reasoning—for problem solving. Three widely accepted forecasting techniques—the Delphi method, trend impact analysis and cross-impact analysis—are briefly reviewed. The hybrid technique developed and the details of the fuzzy management decision support system for scenario analysis are described. Using a case study on the penetration of computer-integrated manufacturing (CIM) in the electronics industry in Singapore as an example, the performance of the system is discussed.

In this paper, we propose a wet lab algorithm for classification of SODAR data by DNA computing. The concept of DNA computing is essentially exploited to generate the classifier algorithm in the wet lab. The classifier is based on a new concept of similarity-based fuzzy reasoning suitable for wet lab implementation. This new concept of similarity-based fuzzy reasoning is different from conventional approach to fuzzy reasoning based on similarity measure and also replaces the logical aspect of classical fuzzy reasoning by DNA chemistry. Thus, we add a new dimension to the existing forms of fuzzy reasoning by bringing it down to nanoscale. We exploit the concept of massive parallelism of DNA computing by designing this new classifier in the wet lab. This newly designed classifier is very much generalized in nature and apart from SODAR data, this methodology can be applied to other types of data also. To achieve our goal we first fuzzify the given SODAR data in a form of synthetic DNA sequence which is called fuzzy DNA and which handles the vague concept of human reasoning. In the present approach, we can avoid the tedious choice of a suitable implication operator (for a particular operation) necessary for the classical approach to fuzzy reasoning based on fuzzy logic. We adopt the basic notion of DNA computing based on standard DNA operations. We consider double stranded DNA sequences, whereas, most of the existing models of DNA computation are based on single stranded DNA sequences. In the present model, we consider double stranded DNA sequences with a specific aim of measuring similarity between two DNA sequences. Such similarity measure is essential for designing the classifier in the wet lab. Note that, we have developed a completely new measure of similarity based on base pair difference which is absolutely different from the existing measure of similarity and which is very much suitable for expert system approach to classifier design, using DNA computing. In the present model of DNA computing, the end result of the wet lab algorithm produces multi valued status which can be linguistically interpreted to match the perception of an expert.

In this paper, we propose a wet lab algorithm for prediction of visibility under radiation fog by DNA computing. The model is based on a concept of similarity based fuzzy reasoning suitable for wet lab implementation. The concept of similarity based fuzzy reasoning using DNA sequences is different from conventional approach to fuzzy reasoning. It replaces the logical aspect of classical fuzzy reasoning by DNA chemistry. By the proposed algorithm the tedious job to choose suitable implication operator, which is absolutely necessary for classical fuzzy reasoning, can be avoided. If the fuzzified forms of five observed parameters, i.e. dew point, dew point spread, the rate of change of dew point spread per day, wind speed and sky condition are given, the newly proposed algorithm efficiently predicts the possibility of visibility under radiation fog. The final result of the wet lab algorithm, which is in form of fuzzy DNA, produces multi valued status which can be linguistically interpreted to match the perception of an expert.

Petri nets are a powerful tool for visual representation of complex software engineering and knowledge engineering problems, and for analysis of their dynamic behavior. This survey consists mainly of practical examples, which include the use of time Petri nets. Petri nets have well-defined semantics, and can be used to interpret textual languages, particularly for communication and coordination. We explore one such application in detail. The nets can grow too large for human comprehension; some suggestions are given on how to deal with this problem. We also look at fuzzy reasoning based on Petri nets.

In Bayesian networks as well as in knowledge-based systems, uncertainty in propositions can be represented by various degrees of belief encoded by qualitative values. In this paper, we present an approach of classical probability theory in the particular case where the set of probability degrees [0,1] is replaced by a totally ordered set of symbolic values. First, we define the four elementary operations (addition, subtraction, multiplication and division) allowing to manipulate these symbolic degrees of uncertainty, then we propose an axiomatic. The properties obtained from this axiomatic allow to show that our theory constitutes a qualitative approach for processing uncertain statements. The obtained results are usable in inferential processes as well as in Bayesian networks.