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The fuzzy set theory as one of the key agents of artificial intelligence has been used to deal with vagueness and imprecision during the decision-making process. The software requirements selection is a multicriteria decision making problem which has a key importance for several software development companies. Few methods have been developed to select the software requirements from the list of the elicited requirements using fuzzy analytic hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) methods. Based on our review, we found that little attention is given on the comparison between the fuzzy AHP and fuzzy TOPSIS methods in the context of the software requirements selection problem. To address this issue, this paper presents a comparative analysis of fuzzy AHP and fuzzy TOPSIS methods by considering the small and large set of requirements of an institute examination system based on the following factors: agreement measure, time complexity, rank reversal issue, and number of judgments by decision makers. Based on the comparative study, we found that both fuzzy AHP and fuzzy TOPSIS methods produce the same set of functional requirements based on agreement measure metric in both dataset-1 and dataset-2. Fuzzy AHP requires less time to generate the ranking order in dataset-1; and fuzzy TOPSIS performs better then fuzzy AHP in dataset-2. Fuzzy AHP causes the rank reversal issue; and there is no rank reversal issue in fuzzy TOPSIS and it produces the consistent results. Fuzzy TOPSIS requires less judgment by decision makers then fuzzy AHP. Finally, we discuss the future research directions in the area of SRs selection problem.

The fuzzy set theory as one of the key agents of artificial intelligence has been used to deal with vagueness and imprecision during the decision-making process. The software requirements selection is a multicriteria decision making problem which has a key importance for several software development companies. Few methods have been developed to select the software requirements from the list of the elicited requirements using fuzzy analytic hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) methods. Based on our review, we found that little attention is given on the comparison between the fuzzy AHP and fuzzy TOPSIS methods in the context of the software requirements selection problem. To address this issue, this paper presents a comparative analysis of fuzzy AHP and fuzzy TOPSIS methods by considering the small and large set of requirements of an institute examination system based on the following factors: agreement measure, time complexity, rank reversal issue, and number of judgments by decision makers. Based on the comparative study, we found that both fuzzy AHP and fuzzy TOPSIS methods produce the same set of functional requirements based on agreement measure metric in both dataset-1 and dataset-2. Fuzzy AHP requires less time to generate the ranking order in dataset-1; and fuzzy TOPSIS performs better then fuzzy AHP in dataset-2. Fuzzy AHP causes the rank reversal issue; and there is no rank reversal issue in fuzzy TOPSIS and it produces the consistent results. Fuzzy TOPSIS requires less judgment by decision makers then fuzzy AHP. Finally, we discuss the future research directions in the area of SRs selection problem. [ABSTRACT FROM AUTHOR]

Due to importance of measuring the degree of resemblance, the similarity measure is widely adopted in various areas of the information systems (e.g., medical informatics and information retrieval) and in several applications like medical diagnostic, image processing, and pattern recognition. However, most of the existing similarity measures focus mainly on the degree of similarity without consulting expert(s) about the results. In this paper, an efficient tool for measuring similarity and agreement of objects that embeds experts’ opinions is proposed to assess similarity among features and agreement of opinions among experts. To obtain such robust measuring tool, three construction steps were followed. Firstly, adapting soft expert set as a general structure that consists of four components: objects, attributes, experts, and experts’ opinions. Secondly, representing the soft expert set, without losing stored information, in such a way as to fit the proposed similarity-agreement measure and make it simpler and more meaningful than the similar existing measures. Thirdly, axiomatizing the similarity-agreement measure for the case of two experts to simplify the model. Further, a diagnostic prediction application and its algorithm is discussed in this context, along with analysis of the experimental results. Analysis of performance of the proposed similarity-agreement measure revealed that it has high accuracy, sensitivity, and value of the F-measure and that it has better performance than existing state-of-the-art tools.

In oncology clinical trials, progression-free survival (PFS), generally defined as the time from randomization until disease progression or death, has been a key endpoint to support licensing approval. In the U.S. Food and Drug Administration guidance for industry, May 2007, concerning the PFS as the primary or co-primary clinical trial endpoint, it is recommended to have tumor assessments verified by an independent review committee blinded to study treatments, especially in open-label studies. It is considered reassuring about the lack of reader-evaluation bias if treatment effect estimates from the investigators' and independent review committees' evaluations agree. The agreement between these evaluations may vary for subjects with short or long PFS, while there exist no such statistical quantities that can completely account for this temporal pattern of agreements. Therefore, in this paper, we propose a new method to assess temporal agreement between two time-to-event endpoints, while the two event times are assumed to have a positive probability of being identical. This method measures agreement in terms of the two event times being identical at a given time or both being greater than a given time. Overall scores of agreement over a period of time are also proposed. We propose a maximum likelihood estimation to infer the proposed agreement measures using empirical data, accounting for different censoring mechanisms, including reader's censoring (event from one reader dependently censored by event from the other reader). The proposed method is demonstrated to perform well in small samples via extensive simulation studies and is illustrated through a head and neck cancer trial. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Introduction: Acquired cranial asymmetry is prevalent in infants today and largely attributed to the supine sleep position recommended for infant safety. There is a risk of permanent cranial asymmetry, so prevention and early detection are important. A prevention project was initiated in Sweden, and an intervention was planned. The aim of this study was to evaluate reliability of assessors judging infant cranial asymmetry in order to evaluate if they could be considered reliable interchangeable assessors in the planned intervention., Materials and Methodology: Five assessors were taught how to assess infant cranial asymmetry using illustrated severity assessments. They were intra-rater and inter-rater reliability tested by taking a photograph test-retest and an infant test. Agreement matrices were devised to illustrate assessor agreement based on both type and degree of cranial asymmetry. Agreement based on degree of asymmetry was analyzed by calculating AC2 using quadratic weights. Results were adjusted to arrive at the perceived genuine agreement and interpreted according to Landis and Koch's strength of agreement intervals., Results: In the photograph test, mean percentage of perfect intra-rater agreement was 73. Adjusted mean intra-rater AC2 was 0.69 [0.63; 0.76], and adjusted inter-rater AC2s were 0.72 [0.64; 0.81] and 0.71 [0.63; 0.79]. In the infant test, the adjusted inter-rater AC2 was 0.73 [0.60; 0.87]. Results indicate substantial strength of assessor agreement., Conclusion: Assessors were reliable and interchangeable. In a larger clinical context, results indicate that educating child health care nurses to assess infant cranial asymmetry can be used for early detection.