Analysis of Telephone Call Detail Records based on Fuzzy Decision Tree

Digital evidences can be obtained from computers and various kinds of digital devices, such as telephones, mp3/mp4 players, printers, cameras, etc. Telephone Call Detail Records （CDRs） are one important source of digital evidences that can identify suspects and their partners. Law enforcement authorities may intercept and record specific conversations with a court order and CDRs can be obtained from telephone service providers. However, the CDRs of a suspect for a period of time are often fairly large in volume. To obtain useful information and make appropriate decisions automatically from such large amount of CDRs become more and more difficult. Current analysis tools are designed to present only numerical results rather than help us make useful decisions. In this paper, an algorithm based on Fuzzy Decision Tree （FDT） for analyzing CDRs is proposed. We conducted experimental evaluation to verify the proposed algorithm and the result is very promising.

A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps

In the aluminum reduction process, aluminum uoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic ef ciency. Making the decision on the amount of AlF3 addi- tion (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into con- sideration a variety of interrelated functions;in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is dif cult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have dif culty covering these complex causalities. In this work, a data and knowl- edge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy deci- sion trees, which are used to amend the initial structure provided by experts. The state transition algo- rithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA.

Quality of experience models for network video quality

Nowadays,the service of network video is increasing explosively.But the quality of experience(QoE)model of network video quality is not stable.The video quality may be impaired by many factors.This paper proposes QoE models for network video quality.It consists of two components:1)the perceptual video quality model considering the impair factors related to video content as well as distortion caused by content and transmission.Next the model is built through a decision tree using a set of measured features form the network video.This proposed model can qualitatively give the grade of video quality and improve the accuracy of prediction.2)Based on the above model,another model is proposed to give the concrete objective score of video quality.It also considers original impair factors and predicts the video quality using fuzzy decision tree.The two models have their own advantages.The first model has a good computational complexity;the second model is more precise.All the models are simulated by actual experiments.They can improve the accuracy of objective model.The detail results are shown.