The diagnosis of the Healthcare systems are playing prominent role with the advancement of latest technologies. Decision support systems may generate fruitful outcomes for better diagnosis for Breast cancer. The present context of the paper describes about BCD-NFIS that merely reduces usage of featured datasets using Fuzzy networks and produces enhanced accuracy of 98.24% much better results than the older approaches. The BCD-NFIS uses the methodology of Inference systems, Neural Fuzzy logic and BCD to overcome the problems. This would be very much helpful for rising physicians to simplify the diagnosis patterns through exploiting Information technology for Breast cancer.

The effective treatment of cancer is not very easy since diagnosis of cancer involves many stages of treatment with gradually changing lifestyles. Physicians play vital role in identifying the correct cause and feel ambiguity for making perfect decisions about hundreds of data available from the internet resource. IDA (Intelligent Data Analysis) which is a part from Data Mining techniques is quiet useful to most of the physicians for decision making about types of cancers. IDA facilitates physicians to classify, detect and analyze the cancer outcome to patients. Healthcare Management System also aids the practitioners to practically search, analyze and compare the result analysis of the patient with existing data in the HMS and guide proper treatment to the cancer affected patient. Health care data analysis comprises enormous data with diversity of health information. One among the most important points that pull down the practitioner's confidence is that utility of latest software and most sophisticated computing machines. This put them in to the state of confusion for proper and elegant decision making for treating the cancer affected patients. Problems in user interaction, lack of awareness in data mining, improper knowledge in electronic guidelines makes physicians to work with old methods of treatment. Traditional medical practicing and modern methods of computing do not match either because of ignorance. IDA and HMS have significant impact for cancer treatment with speedy diagnosis and faster recovery. This also shows great impact on costs, clinical outcomes and proper guidelines for clinical approach. The prime motto of this survey article is to analyze the survey application, bring out the importance of comparison strategies of IDA to improve decision making for medical practitioner for effective cancer treatment.

Web data mining is a rising examination territory where taking out information is an essential job and a range of algorithms has been projected with a specific end goal to comprehend the an assortment of issues identified with web mining from available dataset. Here, we focus Frequent Pattern-Growth algorithm for data mining. Concerning FP-Growth, the efficiency is insufficient since mining progression is depends on large tree-frame data structure by internal memory estimate. We focuses on server monitor documents to find web convention forms of websites using web utilization mining and in exacting spotlights. Here, we had the practice to work with the projected strategy which could conceivable to eradicate the disadvantage of restriction of the presented rehearse in the area of web mining. An assortment of web usage mining practice can advance effort on numerous areas of scientific, medical & social media applications to advance toward for the research & security united zone. A briefed outline development system could help in gathering additional information on utilizing line up algorithm which shows the information state-plans effectually.

No abstract

No abstract

A novel approach to Contact Map Overlap (CMO) problem is proposed using the two dimensional clusters present in the contact maps. Each protein is represented as a set of the non-trivial clusters of contacts extracted from its contact map. The approach involves finding matching regions between the two contact maps using approximate 2D-pattern matching algorithm and dynamic programming technique. These matched pairs of small contact maps are submitted in parallel to a fast heuristic CMO algorithm. The approach facilitates parallelization at this level since all the pairs of contact maps can be submitted to the algorithm in parallel. Then, a merge algorithm is used in order to obtain the overall alignment. As a proof of concept, MSVNS, a heuristic CMO algorithm is used for global as well as local alignment. The divide and conquer approach is evaluated for two benchmark data sets that of Skolnick and Ding et al. It is interesting to note that along with achieving saving of time, better overlap is also obtained for certain protein folds.

Road extraction from satellite images has several Applications such as geographic information system (GIS). Having an accurate and up-to-date road network database will facilitate transportation, disaster management and GPS navigation. Most active field of research for automatic extraction of road network involves semantic segmentation using convolutional neural network (CNN). Although they can produce accurate results, typically the models give up performance for accuracy and vice-versa. In this paper, we are proposing architecture for semantic segmentation of road networks using Atrous Spatial Pyramid Pooling (ASPP). The network contains residual blocks for extracting low level features. Atrous convolutions with different dilation rates are taken and spatial pyramid pooling is performed on these features for extracting the spatial information. The low level features from residual blocks are added to the multi scale context information to produce the final segmentation image. Our proposed model significantly reduces the number of parameters that are required to train the model. The proposed model was trained on the Massachusetts roads dataset and the results have shown that our model produces superior results than that of popular state-of-the art models.

No abstract