No abstract

In this decade, SM Es have experienced substantial growth. According to the results of research conducted by the Retail Research Center, this sector experienced a growth rate of 18.6% in Europe in 2015 and 16.7% in 2016. The increasing co mpetition in the SM Es demanded this effort to improve techniques and strategies to maintain customer satisfaction levels to continue to increase .[1]. The SM Es sector has an important role in the country' s economy, especially Indonesia. They have proven their existence in the past few years. SM Es have a proportion of 99.99% of the total business actors in Indonesia or as many as 56.54 million units. Based on data achieved by SMEs, in 2013 the Abstract: The CLV model is a measure of customer profit fo r a co mpany that can be used to evaluate the future value of a customer. The CLV model is a measure of customer profit fo r a co mpany that can be used to evaluate the future value of a customer. This study aims to obtain Customer Lifetime Value (CLV) in each customer segment. Grouping uses the K-Means Clustering method based on the LRFM model (Length, Recency, Frequency, Monetary). The cluster formation process uses the Elbow Method and SSE with the best number of clusters = 2 clusters. CLV values are generated fro m the mu ltip lication of the results of normalizat ion of LRFM and the LFRM weight values are then summed, and carried out on each cluster that has been formed. The highest ranking among the 2 clusters is at the second cluster with the CLV value being far the h ighest from the other cluster average of 0.362. Based on LRFM matrix, this cluster has a high loyalty value with the symbol LRFM L ↑ R ↑ F ↑ M ↑ wh ich is a loyal customer (the best segment that has high customer loyalty value). Based on the LRFM symbol, the company can make a strategy to retain customers and acquire customers to become loyal customers with high profitability.

No abstract

In this study, we develop a comprehensive design process of granular fuzzy rule-based systems. These constructs arise as a result of a structural compression of fuzzy rule-based systems in which a subset of originally existing rules is retained. Because of the reduced subset of the originally existing rules, the remaining rules are made more abstract (general) by expressing their conditions in the form of granular fuzzy sets (such as interval-valued fuzzy sets, rough fuzzy sets, probabilistic fuzzy sets, etc.), hence the name of granular fuzzy rule-based systems emerging during the compression of the rule bases. The design of these systems dwells upon an important mechanism of allocation of information granularity using which the granular fuzzy rules are formed. The underlying optimization consists of two phases: structural (being of combinatorial character in which a subset of rules is selected) and parametric (when the conditions of the selected rules are made granular through an optimal allocation of information granularity). We implement the cooperative particle swarm optimization to solve optimization problem. A number of experimental studies are reported; those include fuzzy rule-based systems.

No abstract

Machine vision is very important for ensuring the success of intelligent transportation systems, particularly in the area of road maintenance. For this reason, many studies had been focusing on automatic image-based crack detection as a replacement for manual inspection that had depended on the specialist's knowledge and expertise. In the image processing technique, the pre-processing and edge detection stages are important for filtering out noises and in enhancing the quality of the edges in the image. Since threshold is one of the powerful methods used in the edge detection of an image, we have therefore proposed a modified Otsu-Canny Edge Detection Algorithm in the selection of the two threshold values as well as implemented a multi-resolution level fixed partitioning method in the analysis of the global and local threshold values of the image. This is then followed by a statistical measure in selecting the edge image with the best global threshold. This study had utilized the road crack image dataset that were obtained from Crackforest. The results had revealed the proposed method to not only perform better than the conventional Canny edge detection method but had also shown the maximum value derived from the local threshold of 5x5 partitioned image outperforming the other partitioned scales.

The importance of flood damage assessment has been highlighted by the government as well as by many researchers. Nevertheless, the effort in performing the damage studies is less to be found due to the lack of awareness and some other limitations related to the data and its methodologies. The flood damage data in fact is part of an essential ingredient in developing the flood mitigation policy as well as in evaluating the effectiveness of the current flood reduction measures. However, unlike other kinds flood risk quantification study, damage assessment is the one that less concerned by the researchers. This paper has mainly provides a brief introduction towards the flood damage assessment and certain essential element need to be taken into consideration have been highlighted. An analysis of previous flood damage assessment studies and discussion towards some critical issues are presented in this paper other than proposing granular fuzzy system for enhancement in flood assessment for quality risk analysis.

In the ever-evolving vibrant landscape of our times, it is crucial that a peaceful environment is ensured taking into account all the likely ecological parameters along with humidity and temperature while conserving energy. Thus, besides mechanical and electric control systems, it has become vital to ensure that artificial intelligence (AI) is assimilated and deployed into the systems so as to raise the well-being of the environment. By disseminating intelligence across the building by utilizing the new internet of things (IoT) technology, along with control formats, local open standard data, AI algorithms, and cloud-based predictive analytics, the heating, ventilation, and air conditioning (HVAC) mechanism renders the capability to acclimatize to use patterns, alterations in use patterns, and equipment breakdown. By tracing human activity coupled with analysis of noise, energy, and temperature in the building, its occupants and facility managers can obtain vital insights for planning, optimum use of space, and behavioral changes, in turn ensuring more content and safer inhabitants and considerably more efficient structures. Moreover, fuzzy modeling shows its applicability factor with the execution of human rationale and reasoning with if-then rules as attained from the system’s input-output info for model setup and training. Additionally, it presents advantages pertaining to predictive functions for tackling nonlinearity and uncertainty as well as studying the capability of the models recommended. Thus, the multi-dimensional model recommended in this study outlines a system architecture as an implementation methodology and how it harmonizes prevailing systems while offering comprehensive knowledge to HVAC systems for the accomplishment of lower energy consumption and inhabitant safety and well-being on the basis of the fuzzy modeling. With tolerance for CO2 discharges moving towards zero, the recommended multi-dimensional model provides substantial advantages for the HVAC sector for meeting the essential objectives while taking into account enhanced sustainability in vibrant and nonlinear environments for enhancing the accuracy and fairness of the assessment outcomes.