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The daily responsibilities of e-commerce enterprises, such as operational preparation and market assessment, are greatly impacted by sales forecasting. Since the market is constantly shifting, e-commerce enterprises must immediately solve the important difficulty of effectively forecasting sales. Traditional forecasting techniques also have limitations. This study offers a novel fuzzy logic-based method for improving sales forecasting’s handling of uncertainty. This study gathers a wide range of data from multiple sources, such as transaction logs, analytics of consumer behavior, and market trends. To guarantee its quality and dependability, the gathered data are pre-processed by using min–max normalization and glowworm swarm optimization (GSO) was employed for feature selection. This research develops a fuzzy logic multi-criteria (FLMC) algorithm that takes into account a number of influencing elements, such as customer behavior, customer demographics, market trends, website traffic and engagement, product availability, and seasonal fluctuations, by leveraging fuzzy logic’s ability to simulate imprecision and ambiguity. The suggested algorithm uses a multi-criteria decision-making process to assess and evaluate these variables, producing useful information that helps firms maximize their marketing, pricing, and inventory control initiatives. The efficacy of the FLMC algorithm in enhancing forecasting accuracy is demonstrated empirically. The findings demonstrate how the algorithm could be used to mitigate the unpredictability found in e-commerce settings, promoting better decision-making and increasing operational effectiveness.

Geological-mining projects are usually associated with relatively high risk and uncertainty in many aspects, including geological, mining, ecologic, economic, market, legal and social conditions. A mineral deposit is an underground natural resource and hence it is difficult to unequivocally predict the actual results of its discovery. Depending on the extent of the resource, the operation of the mine can extend to a few decades. It is necessary to conduct investment actions in successive stages and to evaluate the results of the work stage by stage. This reduces the investment risk and facilitates the decision-making process. In this paper, the use of a specific kind of game, the so-called "game against Nature," is suggested before a final decision on deposit development is made. This methodology was tested on the example of one of the zinc-lead ore deposits in the Silesia-Cracow region. Apart from supporting the decision-making process, this methodology offers the means to evaluate further research and costs which may be incurred for obtaining supplementary information related to the ore deposit parameters, specifically its reserves.

The specification-based testing can be employed to evaluate software functionalities without knowing program code. Decisions are the primary form of the pre- and post-conditions in formal specifications. This work expatiates on logic coverage criteria for specification-based testing at great length. It proposes and then expounds mask logic coverage criteria to solve the problems which existing determinant logic coverage criteria cannot solve. A feasible test case generation algorithm based on mask logic coverage criteria is developed. The test cases satisfying mask logic coverage criteria can detect those errors caused by the mask property of conditions. An experiment is conducted to compare MC/DC, RC/DC and two mask logic coverage criteria (RMCC and GMCC) on their test effectiveness and fault detection ability. It also elaborates on the constraint among conditions, how to decompose and compose a complicated decision, and the relationship among decisions. All these can respectively clarify the coupling problem among conditions, the multiple occurrences of a condition in a decision, and the location of a decision in a specification or program. Additionally, coverage criteria including full true decision coverage, full false decision coverage, all sub-decisions coverage, unique condition true coverage and unique condition false coverage are proposed. The test sets satisfying these criteria can detect respectively different types of errors. Finally, the hierarchical subsumption relation is established among these presented coverage criteria and some existing ones, and various applicable scenarios for different coverage criteria are suggested.

In this paper, we focus on multi-criteria decision-making problems. We propose a model based on influence diagrams; this model is able to handle uncertainty, represent interdependencies among the different decision variables and facilitate communication between the decision-maker and the analyst. The particular structure of the proposed model makes it possible to take into account the alternatives described by an attribute set, the decision-maker's characteristics and preferences, and other information (e.g., internal or external factors) that influence the decision. Modeling the decision problem in terms of influence diagrams requires a lot of work to gather expert knowledge. However, once the model is built, it can be easily and efficiently used for different instances of the decision problem. In fact, using our model simply requires entering some basic information, such as the values of internal or external factors and the decision-maker's characteristics. Our model also defines the importance of each criterion in terms of what is known about the decision maker, the quality index and the utility of each alternative.

Signal processing techniques will lean on blind methods in the near future, where no redundant, resource allocating information will be transmitted through the channel. To achieve a proper decision, however, it is essential to know at least the probability density function (PDF), which to estimate is classically a time consumpting and/or less accurate hard task that may make decisions to fail. This paper describes the design of a quantum assisted PDF estimation method also by way of an example, which promises to achieve the exact PDF by proper setting of parameters in a very rapid way.

There are many interests in developing life-like robots, or robots which are both intelligent and autonomous. And, one obvious characteristics of life-like creatures is that they can autonomously develop and learn during their life span. Such abilities obviously depend on the ways of designing human-like minds. Then, a fundamental question is how to devise the innate, or built-in, principles behind the blueprint of a human-like mind, and to apply these findings to guide the design of the mind of life-like robots. In the literature, there are two schools of thoughts. One advocates the study of the nervous systems of biological brains (e.g. human brain) until the discovery of the blueprint of a mind. The second approach is to follow the path of invention and validation until the full understanding of physical principles which enable the design of an artificial mind that is as good as a biological mind. This paper embraces the second approach, and aims at formulating a new ground which could guide the design of the minds of life-like robots at various stages. In particular, the discussion is focused on answering the question of what life is from an engineering point of view. And, we approach the answer by examining the key steps of evolution from non-life to life. In this paper, five key steps of evolution from non-life to life will be discussed in detail. They are embodiment of energy flow, embodiment of signal flow, embodiment of knowledge flow, embodiment of decision flow, and embodiment of awareness flow. These findings are grounded on our engineering works toward the development of low-cost humanoid (LOCH) robot, and offer a unique perspective and an engineering basis. Whenever possible, the discussions in this paper are supported by real results of experiments on real robots.

The security of both technical and trade credit factors becomes a serious bottleneck that affects the current development of e-business. Honest behavior is one of the important aspects of trade credit safety. It is a vital step to establish and maintain a safe credit system. In this paper, we analyze the characteristic of the credit in e-business and establish a motivation model that violates honest behavior in e-business. We evaluate the credit, the mode of evaluation and the behavior of the credit in e-business by using game theory. We present the factors that affect the justness of credit evaluation in e-business.

The rapid adoption of artificial intelligence in automating human-centred tasks has accentuated the importance of interpretable decisions. The Belief-Rule-Base (BRB) is a hybrid expert system that can accommodate human knowledge and capture nonlinear causal relationships as well as uncertainty. This paper presents the strategy to interpret BRB locally for a single instance to understand the decision-making process by the importance of activated rules and attributes and globally to understand most important rules and attributes in an entire rule base.

We try to provide a tentative assessment of the role of fuzzy sets in decision analysis. We discuss membership functions, aggregation operations, linguistic variables, fuzzy intervals and valued preference relations. The importance of the notion of bipolarity and the potential of qualitative evaluation methods are also pointed out. We take a critical standpoint on the state of the art, in order to highlight the actual achievements and point out research directions for the future.