In this paper, the prediction of compressive cement strength using the fuzzy linear regression (FLR) and adaptive neuro-fuzzy inference system (ANFIS) methods was studied. Specifically, an accurate prediction method is needed as the modeling of cement strength is a difficult task, which is based on its composite nature. However, many approaches are widely implemented in strength-predicting problems, such as the artificial neural network (ANN), Mamdani fuzzy rules in MATLAB, FLR and ANFIS models. Applying these methods and comparing the results with the corresponding observed ones, we concluded that the ANFIS method successfully decreased the level of uncertainty in predicting cement strength, as the average percentage error level was extremely low. Although the FLR method had the highest average percentage error level compared with the other methods, it provides a standard equation to estimate the output values by using symmetric triangular fuzzy numbers and determines the most important factor in increasing compressive strength, in contrast to ANFIS and ANN, which are black box models, and to the fuzzy method, which uses rules without providing the specific way by which the results come out. Thus, ANFIS and FLR are appropriate methods for dealing with engineering mathematical models by using fuzzy logic.

The major drawback of the classic approaches for project appraisal is the lack of the possibility to handle change requests during the project’s life cycle. This fact incorporates the concept of uncertainty in the estimation of this investment’s worth. To resolve this issue, the authors use fuzzy numbers, possibilistic moments of fuzzy numbers and the hybrid (fuzzy statistic) fuzzy estimators’ method in order to introduce a fuzzy possibilistic version of the expanded net present value method (FPeNPV). This approach consists of two factors: the fuzzy possibilistic NPV and the fuzzy option premium. For the estimation of the fuzzy NPV, some basic assumptions are taken into consideration: (1) the opportunity cost of capital, used as the present value interest factor calculated through the weighted average cost of capital (WACC), (2) the equity cost, determined through the possibilistic set-up of the capital asset pricing model CAPM, and (3) the inflation factor, also included in the estimation of the NPV. The fuzzy estimators’ method is used for the computation of the fuzzy option premium. An algorithm of nine major steps leads to the computation of the FPeNPV. This gives the administration the opportunity to adapt to potential changes in the company’s internal and external environments. In this way, the symmetry between the planning and execution phase of a project can be reinstated. The results validate the statement that fuzzy and intelligent methods remain valuable tools to express uncertainty in various scientific areas. Finally, an illustrative example aims at a thorough comprehension of this new approach of the expanded NPV method.

Image conversion of byte-level data, or binary visualisation, is a relevant approach to security applications interested in malicious activity detection. However, in practice, binary visualisation has always been seen to have great limitations when dealing with large volumes of data, and would be a reluctant candidate as the core building block of an intrusion detection system (IDS). This is due to the requirements of computational time when processing the flow of byte data into image format. Machine intelligence solutions based on colour tone variations that are intended for pattern recognition would overtax the process. In this paper, we aim to solve this issue by proposing a fast binary visualisation method that uses Fuzzy Set theory and the H-indexing space filling curve. Our model can assign different colour tones on a byte, allowing it to be influenced by neighbouring byte values while preserving optimal locality indexing. With this work, we wish to establish the first steps in pursuit of a signature-free IDS. For our experiment, we used 5000 malicious and benign files of different sizes. Our methodology was tested on various platforms, including GRNET’s High-Performance Computing services. Further improvements in computation time allowed larger files to convert in roughly 0.5 s on a desktop environment. Its performance was also compared with existing machine learning-based detection applications that used traditional binary visualisation. Despite lack of optimal tuning, SAGMAD was able to achieve $91.94\%$ accuracy, $90.63\%$ precision, $92.7\%$ recall, and an F-score of $91.61\%$ on average when tested within previous binary visualisation applications and following their parameterisation scheme. The results exceeded malware file-based experiments and were similar to network intrusion applications. Overall, the results demonstrated here prove our method to be a promising mechanism for a fast AI-based signature-agnostic IDS.