Abstract. Hematological malignancies are the types of cancer that affect blood, bone marrow and lymph nodes. As these tissues are naturally connected through the immune system, a disease affecting one of them will often affect the others as well. The hematological malignancies include; Leukemia, Lymphoma, Multiple myeloma. Among them, leukemia is a serious malignancy that starts in blood tissues especially the bone marrow, where the blood is made. Researches show, leukemia is one of the common cancers in the world. So, the emphasis on diagnostic techniques and best treatments would be able to provide better prognosis and survival for patients. In this paper, an automatic diagnosis recommender system for classifying leukemia based on cooperative game is presented. Through out this research, we analyze the flow cytometry data toward the classification of leukemia into eight classes. We work on real data set from different types of leukemia that have been collected at Iran Blood Transfusion Organization (IBTO). Generally, the data set contains 400 samples taken from human leukemic bone marrow. This study deals with cooperative game used for classification according to different weights assigned to the markers. The proposed method is versatile as there are no constraints to what the input or output represent. This means that it can be used to classify a population according to their contributions. In other words, it applies equally to other groups of data. The experimental results show the accuracy rate of 93.12%, for classification and compared to decision tree (C4.5) with (90.16%) in accuracy.The result demonstrates that cooperative game is very promising to be used directly for classification of leukemia as a part of Active Medical decision support system for interpretation of flow cytometry readout. This system could assist clinical hematologists to properly recognize different kinds of leukemia by preparing suggestions and this could improve the treatment of leukemic patients.

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Cancer is a term used for diseases in which abnormal cells divide without control and invade other tissues. Cancer types can be grouped into broader categories including Leukemia, Carcinoma, Sarcoma, Lymphoma and Myeloma, Central nervous system cancers among them, Leukemia is a form of serious cancers that starts in blood tissue such as the bone marrow where all the blood is made. It is one of the leading causes of death in the world. So, the importance of diagnostic techniques is manifested. Application of these techniques would be able to decrease the mortality rate from leukemia. In this paper, an automatic system for classifying leukemia based on game theory is presented. The aim of this research is to apply game theory in order to classify leukemia into eight classes. In other words, cooperative game is used for classification according to different weights assigned to the markers. Through out this paper, we work on real data (304 samples) taken from different types of leukemia that have been collected at Iran Blood Transfusion Organization (IBTO). The modeling system can be used to model and classify a population according to their contributions. In the other words, it applies equally to other groups of data. The results show that the highest classification accuracy (98.44%) is obtained for the proposed model. So, it is hoped that game theory can be directly used for classification in the other cases.

Over the years, detecting stable communities in a complex network has been a major challenge in network science. The global and local structures help to detect communities from different perspectives. However, previous methods based on them suffer from high complexity and fall into local optimum, respectively. The Four-Stage Algorithm (FSA) is proposed to reduce these issues and to allocate nodes to stable communities. Balancing global and local information, as well as accuracy and time complexity, while ensuring the allocation of nodes to stable communities, are the fundamental goals of this research. The Four-Stage Algorithm (FSA) is described and demonstrated using four real-world data with ground truth and three real networks without ground truth. In addition, it is evaluated with the results of seven community detection methods: Three-stage algorithm (TS), Louvain, Infomap, Fastgreedy, Walktrap, Eigenvector, and Label propagation (LPA). Experimental results on seven real network data sets show the effectiveness of our proposed approach and confirm that it is sufficiently capable of identifying those communities that are more desirable. The experimental results confirm that the proposed method can detect more stable and assured communities. For future work, deep learning methods can also be used to extract semantic content features that are more beneficial to investigating networks.

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