Nowadays, marketing serves the purpose of maximizing customer lifetime value (CLV) and customer equity, which is the sum of the lifetime values of the company’s customers. But, CLV calculation encounters some difficulties which limit the usage of this technique. Nonetheless, companies looking for methods to know how to calculate their customers’ CLV. In this paper, fuzzy Classification rules were used to determine customers’ CLV and segment them based on recency, frequency and monetary (RFM) measures. Data required for applying this method gathered from a steel firm in Iran.

Electricity utility have long sought to identify and reduce energy theft, which represents significant part of non-technical losses. On the other hand, once a fraudulent customer is detected, on-site inspection is necessary for final verification. Since inspecting all customers is expensive, utilities seek to reduce the range of inspection to cases with a higher probability of theft. One way to reduce the scope of inspection is to use artificial intelligence-based methods. An essential challenge here is data imbalance in terms of the ratio of normal to fraudulent customers, which leads to the poor performance of algorithms. In This paper in order to overcome this challenge, assuming that suspicious behavior can be expressed as a mathematical function of normal behavior, in the first stage, the consumption pattern of normal and suspicious customers is categorized using clustering algorithms. Then a deep neural network is trained to model suspicious customers. Next, using trained network, possible theft scenarios for normal costumers are predicted. Finally, a secondary deep neural network is trained to separate the normal and suspicious customers. Assessment of the proposed algorithm for different scenarios on a real data-set with more than 6000 customers and comparison with previous research shows its high performance.

Electricity utility have long sought to identify and reduce energy fraud as a significant part of non-technical losses (NTL). Generally, to determine customer’, s honesty in consumption on-site inspection is vital. Since, inspecting all customers is expensive, utilities look for new ways to reduce inspection’, s range to cases with a higher probability of fraud. One way to reduce the scope of inspection is to use machine learning (ML) algorithms to analysis consumption pattern. But, their performance is not satisfactory due to insufficiency of fraudulent customers. In this paper, a new two-stage ML-based model is presented to detect fraud in distribution network. In the first stage, an Artificial Neural Network (ANN) is trained to model fraudulent customers, which is used to predict theft scenarios for normal consumers to handle data insufficiency. In the second stage, a Support Vector Machine (SVM) classifier is trained to distinguish normal and suspicious consumers. Assessment and comparison of the proposed algorithm to those of conventional models on a real data set with more than 5000 customers shows its high performance.

This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other Classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption.

Classifying customers using data mining algorithms enables banks to keep old customers loyality while attracting new ones. Using decision tree as a data mining technique, we can optimize customer Classification provided that an appropriate decision tree is selected. In this article we have presented an appropriate model to classify customers who use internet banking service. The model is developed based on CRISP- DM standard and we have used real data of Sina bank's Internet bank. In comparison with the other decision trees, ours is based on both optimization and accuracy factors that recognizes new potential internet banking customers using a three level Classification, which is low/medium and high. This is a practical, documentary-based research. Mining customer rules enables managers to make policies based on discovered patterns in order to have a better understanding of what customers really desire.

Due to enchantment in network technology, the worldwide numbers of internet users are growing rapidly. Most of the internet users are using online purchasing from various sites. Due to new online shopping trends over the internet, the seller needs to predict the online customer’ s choice. This field is a new area of research for machine learning researchers. A random forest (RF) machine learning method is a widely used Classification method. It is mainly based on an ensemble of a single decision tree. Online e-commerce websites accumulate a massive quantity of data in large dimensions. A Random Forest is an efficient filter in high-dimensional data to reliably classify consumer behaviour factors. This research article mainly proposed an extension of the Random Forest classifier named “ Weighted Random Forests” (wRF), which incorporates tree-level weights to provide much more accurate trees throughout the calculation as well as an assessment of vector relevance. The weighted random forest algorithm incorporates the C4. 5 method named a “ Hybrid Weighted Random Forest” (HWRF) to forecast online consumer purchasing behaviour. The experimental results influence the quality of the proposed method in the prediction of the behaviour of online buying customers over existing methods.

Credit risk is one of the most important banking risks that is due to not paying principal and interest of loans. Measuring credit risk is important; because not measuring it lead to increasing volume of doubtful accounts and unexpected future losses. In this research a model was proposed that based on linear and nonlinear optimization. This model is finding a separating hyperplane which classify 85 good and bad borrower customers of Iranian’ s bank. This customers are all in Tehran Stock Exchange (TSE). In order to improving the model we used kernel functions, data fuzzification and penalty factors in it. The results show that the best model among linear and nonlinear models with linear, polynomial, sigmoid and RBF kernels, is a linear optimization model with sigmoid kernel function that has accuracy of 80% and recall of 100%.

Selection and allocation in the resilient supply chain, when disruption threatens the supply chain, has become a strategic decision and the focus of many researches; increase in the applications of machine learning in supply chain studies has led to the emergence of faster and reliable decision-making methods, however, in few studies, machine learning has been used to deal with the problem of selecting and assigning suppliers to customers in resilient mode. The purpose of this research is to take a step towards solving this gap by using machine learning algorithms on real world data from the automotive supply chain in Iran. the performance data of 441 suppliers and 7 customers in 1401 was used. In this research, two clustering algorithms have been used to generate labels based on the concept of resilience capacity; Then, since the interpretability of the results was a priority, based on the labeling of the clusters by the experts, the decision tree was used to classify the suppliers based on their performance. The results showed the K-means tree performs better than the DBSCAN tree and criteria such as on-time delivery, capacity, production line stoppage, quality alert, logistics performance and quality performance are effective on suppliers' resilience.

The purpose of this research is to identify and categorize on the other hand insurance customer loyalty programs Using a systematic review approach; so, this research is applied, qualitative, inductive, non-laboratory, and cross-sectional research. The statistical population of the research is loyalty program experts, who are aware of the insurance industry. A judicious and targeted Sampling method to Select the Participants and the theoretical Saturation rule for the Sample size has been used. Finally, the sample size has been extended to 14 by snowball method. In the first stage, the systematic review of programs is identified and in the second stage, the content validity index is used to determine the validity of identified programs in the insurance industry. At the end, selected programs are categorized and validated with the CAPA index. The results show that, according to insurance customers, 15 loyalty programs include awards are as fallow: cash discounts, volume discounts, surcharges and free insurance services (Asantivon), coupons, free non-insurance services, credit cards, insurance policy options, access to sites and apps, Reduced service delivery times, personalized insurance services, non-monetary rewards, preferential behavior, leisure facilities for customers and customer clubs. these programs are grouped into four categories.