Developing a Generic Decision Support System for Poultry Feeding

Poultry breeding of birds such as chickens, ducks, and turkeys, provide humans mainly with meat and eggs. It represents a great share of investment in many countries. Feeding is one of the factors that play an essential role in poultry industry. Moreover, feeding represents a major direct cost in poultry industry. Therefore, it is generally vital to deliver the best animal diet at the minimum cost, to gain more profit. However, mixing feed ingredients turns out to be more troublesome, since several issues got the opportunity to be involved all at the same time. In this paper, the linear programming algorithm is applied and used in a web application to help farmers and producers to find the minimum cost of feeding, considering many factors including: the purpose of breeding, poultry type, growth stage, nutritional requirements and available feedstuffs

Neutrosophic Logic Theory and Applications

Neutrosophic logic is a very powerful and effective concept. It has different application areas due to its ability to capture the stochasticity in many complex real-life use cases. This paper presents the main types of neutrosophic sets. It also surveys and analyzes its most common applications

Modeling Customer Lifetime Value Under Uncertain Environment

Customer lifetime value (CLV) is an essential measure to determine the level of profitability of a customer to a firm. Customer relationship management treats CLV as the most significant factor for measuring the level of purchases and, consequently, the profitability of a given customer. This motivates researchers to compete in developing models to maximize the value of CLV. Dynamic programming models in general—and the Q-learning model specifically—play a significant role in this area of research as a model-free algorithm. This maximizes the long-term future rewards of a certain agent, given their current state, set of possible actions, and the next state of that agent, assuming the customer represents the agent and CLV is their future reward. However, due to the stochastic nature of this problem, it is inaccurate to obtain a single crisp value for Q. In this paper, fuzzy logic and neutrosophic logic shall be utilized to search for the membership values of Q to capture the stochasticity and uncertainty of the problem. Both fuzzy Q-learning and neutrosophic Qlearning were implemented using two membership functions (i.e., trapezoidal, and triangular) to search for the optimal Q value that maximizes the customer\u27s future rewards. The proposed algorithms were applied to two benchmark datasets: The Knowledge Discovery and Data Mining (KDD) cup 1998 direct mailing campaign dataset and the other from Kaggle, related to direct mailing campaigns. The proposed algorithms proved their effectiveness and superiority when comparing them to each other or the traditional deep Q-learning models