sequential

The decision-making styles of educational leaders and faculty members of educational institutions define the attainment of success of the organization. This study is conducted to determine the decision-making styles, based on the 4 decision-making styles such as global, logical, personable and sequential; and better understand how the school decision making process effectively work and affect school management. The study was a quantitative research and applied descriptive survey design in which 60 regular faculty members and 10 educational leaders were guided to complete the decision-making style survey, however, consent was sought ahead prior to their participation in the study. The findings revealed that the dominant decision-making style of both educational leaders and faculty members was logical. This style proves that they want specifics and they need a clear understanding of the possible results of the different choices. The educational leaders and faculty members tend to weigh choices and exercise sound and critical judgment while setting aside personal feelings. The result further shows that, generally, there is no significant difference between the educational leaders' decision-making style and that of the faculty members, however, they just differ on the order of utilizing the global and logical styles. Both are logical thinkers who analyze between options and draw conclusions based on facts they gather, and thus, faculty members more often subscribe to their leaders' decision. Thus, harmoniously achieving organizational objectives can be observed. Decision making skills in academic institution is vital to help increase job satisfaction, achieve goals and harness the potential of everyone inside the organization.

A recommender system supports customers to find information, products, or services (such as music, books, movies, web sites, and digital contents), so it could help customers to make rapid routine decisions and save their time and money. However, most existing recommender systems do not recommend items that are already purchased by the target customer, so are not suitable for considering customers' repetitive purchase behavior or purchasing order. In this research, we suggest a multi-period product recommender system, which can learn customers' purchasing order and customers' repetitive purchase pattern. For such a purpose we applied the Recurrent Neural Network (RNN), which is one of the artificial neural network structures specialized in time series data analysis, instead of collaborative filtering techniques. Recommendation periods are segmented as various time-steps, and the proposed RNN-based recommender system can recommend items by multiple periods in a time sequence. Several experiments with real online food market data show that the proposed system shows higher performance in accuracy and diversity in a multi-period perspective than the collaborative filtering-based system. From the experimental results, we conclude that the proposed system is suitable for multi-period product recommendation, which results in robust performance considering well customers' purchasing orders and customers' repetitive purchase patterns. Moreover, in terms of sustainability, we expect that our study contributes to the reduction of food wastes by inducing planned consumption, and the reduction of shopping time and effort.

Positive and negative sequential patterns mining is used to discover interesting sequential patterns in a incremental transaction databases, and it is one of the essential data mining tasks widely used in various application fields. Implementation of this approach, construct tree for appended transactions (new upcoming data) and will merge this tree with existing tree (tree of existing transactions) to get the Updated tree. Positive and negative sequential Patterns mining is an aim to find more interesting sequential patterns, considering the minimum support of each data item in a sequence database. Generally, the generation order of data elements is considered to find sequential patterns. Positive sequential patterns states that these items were occur with one and another. Actually the absence of certain itemset may imply the appearance of other itemsets as well. The absence of itemsets thus is becoming measurable in many applications. Negative sequential patterns could assist prod...