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Paraneoplastic syndromes (PNS) are uncommon, distinct clinical complications of a primary tumor. Paraneoplastic cerebellar degeneration (PCD) is a PNS that is described as an autoimmune response targeting Purkinje cells in the cerebellum. Ovarian cancer (OC) is one of the most prevalent causes of cancer-related deaths in women. Anti-Yo is the most common onconeural antibody produced in the PCD immune response and is most typically found in ovarian and breast cancer patients. While the current literature highlights the predisposing genetic factors, diagnostic workflows, and treatment options, the pathophysiology of PCD, among other considerations, remains largely unestablished. This review aimed to systematically observe procedural solutions to facilitate an early diagnosis and improve the prognosis of patients with OC-associated PCD. To that end, we examined literature published from 01/01/2015-11/10/2022 indexed in PubMed by using the keywords "paraneoplastic, cerebellar degeneration" combined with "ovarian cancer." Inclusion criteria were met if PCD and OC diagnoses were made and if studies provided adequate patient information. After screening and assessing records for eligibility using the inclusion and exclusion criteria, 18 articles involving 102 patients were included. The typical patient observed in this sample was diagnosed with International Federation of Gynecology and Obstetrics (FIGO) Stage III, high-grade serous carcinoma. The diagnostic workup typically included a clinical evaluation for dysarthria (50%), ataxia (60%), and gait abnormalities (50%), along with multiple imaging modalities and serological findings (90%). Genetic screening for human leukocyte antigen (HLA) haplotype susceptibility for PCD and immune tolerance modulators regulation may also be recommended prior to starting treatment. Findings support the use of corticosteroids (35%) and intravenous immunoglobulin (IVIg) (40%) as viable treatment options for managing PCD in conjunction with systemic therapy for the primary malignancy. A diagnosis of PCD should be considered if a patient has had a malignancy in the past five years with the presence of explicit cerebellar symptoms. This clinical diagnosis can be further supplemented by serologic and radiologic findings. Recognizing PCD symptoms and scheduling genetic and proteomic testing may help with early diagnosis and better prognosis.

Now the world is completely filled with the data. Each every action is generating the huge amount of data called as Big data. Analysis, processing of Big data itself is a big field emerging nowadays called as data science. Ecommerce field is growing much more and people are becoming lazier to walk through shops for purchasing. A huge competation in ecommerce field to attract people towards purchasing by providing them more and more offers. In order to achieve that need to identify the regular customer to shopping websites and providingthem more offers increases the revenue of ecommerce field this is achieved through mining the shopping dataset.This paper deals the technique that identifies the key customer using the centrality measures by considering his transaction data of shopping dataset.

The evolution of technology has a great impact on the telecom industry, which has grown rapidly from telegraph to present high speed network. This rapid growth has resulted in the establishment of many telecom sectors which in turn has given rise to a stiff competition among them. Telecom sectors with improved technology needs to handle the large set of subscribed customer base. Now a days, in addition to acquisition of new customers to increase the company revenue, retaining the old customers is also found to be of much importance. So, all the telecom industries are concentrating on building a best predictive model in order to determine the churn rate. In this paper we mainly concentrate on refining the telecom dataset by applying the Pre-processing, feature selection and feature extraction techniques. The refined dataset is created to provide the prediction accuracy similar to or greater than the original dataset with less computation.

The telecommunication social network analysis (TSNA) recognizes the relationships among customers in multi-relational and heterogeneous network represent as graph, where vertices are the customers of the telecom network and edges representing some relationships. The major task in TSNA is detection of the communities, which is clustering the telecom customers into subgroups. The structure of the subgroup is arbitrary in which the features of networks are extracted. Extracting the communities is a challenging scenario in social networks. This paper addresses the efficient technique for identification of communities in telecommunication social network where the inner structure is significant. Different variety of substructures are recognized and analyzed as the cliques, N-cliques and K-cores. This paper also concentrates on the clique co-membership to identify central and isolated customers in the network. This technique allows telecom companies to utilize the social activities of customers to design customized service strategy and to make best incentives.

Extracting the customers who share similar interests that are connected via set of relationships in Telecom social network is a challenging scenario. This paper addresses an efficient method of building multi-relational and heterogeneous social network for telecom customers and identifying social structures present in the telecommunication network. The telecom social network is constructed by considering multiple attributes and different services provided by the telecom industry. The telecom network is constructed by using adjacency matrix for all the customers. This approach deals with finding the social position of the customer by different measures like centrality, betweenness, density and closeness. The analysis on the centrality measure are made to identify the central and most influential customers in the network, to provide customized services for retention of that customer. This paper also describes the extraction of dynamic patterns, future structures which aids in retention of customer and managing the market requirement more efficiently.