The recent report of WHO shows a remarkable hike in the number of diabetic patients and this will be in the same pattern in the coming decades also. Early identification of diabetes is an important challenge. Data mining has played an important role in diabetes research. Data mining would be a valuable asset for diabetes researchers because it can unearth hidden knowledge from a huge amount of diabetesrelated data. Various data mining techniques help diabetes research and ultimately improve the quality of health care for diabetes patients. This paper provides a survey of data mining methods that have been commonly applied to Diabetes data analysis and prediction of the disease.
We are undoubtedly living in an age where we are exposed to a remarkable array of visual imagery. Nowadays, accepting digital images of official documents is common practice. Image authenticity is important in many social areas. For instance, the trustworthiness of photographs has an essential role in courtrooms, where they are used as evidence. In the medical field, physicians make critical decisions based on digital images. The technology today makes it convenient to quickly exchange contracts, photographs or other documents. While we may have historically had confidence in the integrity of this imagery, today's digital technology has begun to erode this trust. With the advent of low-cost and high-resolution digital cameras, and sophisticated photo editing software, digital images can be easily manipulated and altered. It is possible to change the information represented by an image and create forgeries, which are indistinguishable by naked eye from authentic photographs and documents. In the proposed method Harris Interest Point detector along with SIFT descriptors are used to detect copymove forgery. KD-Tree is used for matching.
Parameter optimization is an ever fresh and less explored research area, which has ample scope for research investigation and to propose novel findings and interpretations. Identification of good parameter values is a highly challenging task which involves tedious and ad hoc course of actions with several heuristic choices. The complexity involved in parameter tweaking is primarily due to the unpredictable and heavily randomized nature of evolutionary algorithmic procedures. In this paper, an attempt was made to tweak the parameters and decision variables of Genetic Algorithm. GA with tweaked parameters was hybridized with Bacterial Foraging Algorithm, and applied to the Job shop and Permutation Flow Shop scheduling problem benchmarks. The results have proven that optimized parameter set tuning has obtained better scheduling performance.
Customer churn has been considered as one of the key issues in the operations of the corporate business sector, as it influences the turnover directly. In particular, the telecom industries are seeking to develop new approaches to predict potential customer to churn. So, it needs the appropriate algorithms to overcome the increasing problem of churn. This work proposed a churn prediction model that employs both strategies of classification and clustering, that helps in recognizing the churn consumers and giving the reasons after the churning of subscribers in the industry of telecom. The process of information gain and fuzzy particle swarm optimization (FPSO) has been executed by the method of feature selection, besides the divergence kernel-based support vector machine (DKSVM) classifier is employed in categorizing churn customers in the proposed approach. In this way, the compelling guidelines on retention have generated since the process plays a vital role in customer relationship management (CRM) to suppress the churners. After the classification process, the churn customers are divided into clusters through the process of fragmenting the data of churning customer. The cluster-based retention offers have provided by the clustering algorithm of hybrid kernel distance-based possibilistic fuzzy local information C-means (HKD-PFLICM), whereas the measurement of distance have accomplished through the kernel functions such as the hyperbolic tangent kernel and Gaussian kernel. The results reveal that proposed churn prediction model (FPSO- DKSVM) produced better churn classification results compared to other existing algorithms such as K-means, flexible K-Medoids, fuzzy local information C-means (FLICM), possibilistic FLICM (PFLICM) and entropy weighting FLICM (EWFLICM).
Article highlights
Customer churn is a major concern in most of the companies as it influences the turnover directly.
The performance of churn prediction has been improved by applying artificial intelligence and machine learning techniques.
Churn prediction plays a crucial role in telecom industry, as they are in the position to maintain their precious customers and organize their Customer Relationship Management.
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