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Implementation of a neuro-fuzzy segmentation process of the MRI data is presented in this study to detect various tissues like white matter, gray matter, csf and tumor. The advantage of hierarchical self organizing map and fuzzy c means algorithms are used to classify the image layer by layer. The lowest level weight vector is achieved by the abstraction level. We have also achieved a higher value of tumor pixels by this neuro-fuzzy approach. The computation speed of the proposed method is also studied. The multilayer segmentation results of the neuro fuzzy are shown to have interesting consequences from the viewpoint of clinical diagnosis. Neuro fuzzy technique shows that MRI brain tumor segmentation using HSOM-FCM also perform more accurate one.

Histogram equalization is a well-known technique used for contrast enhancement. The global HE usually results in excessive contrast enhancement because of lack of control on the level of contrast enhancement. A new technique named modified histogram equalization using real coded genetic algorithm (MHERCGA) is aimed to sweep over this drawback. The primary aim of this paper is to obtain an enhanced method which keeps the original brightness. This method incorporates a provision to have a control over the level of contrast enhancement and applicable for all types of image including low contrast MRI brain images. The basic idea of this technique is to partition the input image histogram into two subhistograms based on a threshold which is obtained using Otsu's optimality principle. Then, bicriteria optimization problem is formulated to satisfy the aforementioned requirements. The subhistograms are modified by selecting optimal contrast enhancement parameters. Finally, the union of the modified subhistograms produce a contrast enhanced and details preserved output image. While developing an optimization problem, real coded genetic algorithm is applied to determine the optimal value of contrast enhancement parameters. This mechanism enhances the contrast of the input image better than the existing contemporary HE methods. The quality of the enhanced brain image indicates that the image obtained after this method can be useful for efficient detection of brain cancer in further process like segmentation, classification, etc. The performance of the proposed method is well supported by the contrast enhancement quantitative metrics such as discrete entropy and natural image quality evaluator.

Wireless sensor networks are highly indispensable for securing network protection. Highly critical attacks of various kinds have been documented in wireless sensor network till now by many researchers. The Sybil attack is a massive destructive attack against the sensor network where numerous genuine identities with forged identities are used for getting an illegal entry into a network. Discerning the Sybil attack, sinkhole, and wormhole attack while multicasting is a tremendous job in wireless sensor network. Basically a Sybil attack means a node which pretends its identity to other nodes. Communication to an illegal node results in data loss and becomes dangerous in the network. The existing method Random Password Comparison has only a scheme which just verifies the node identities by analyzing the neighbors. A survey was done on a Sybil attack with the objective of resolving this problem. The survey has proposed a combined CAM-PVM (compare and match-position verification method) with MAP (message authentication and passing) for detecting, eliminating, and eventually preventing the entry of Sybil nodes in the network. We propose a scheme of assuring security for wireless sensor network, to deal with attacks of these kinds in unicasting and multicasting.

Nowadays outcome‐based methods are adapted in e‐learning system to meet the need of the novel development of e‐learning systems for improved web‐based retrieval results. Typically, knowledge retrieval process is denoted by the production rule, frame, semantic network, fuzzy logic, predicate logic, and group of skill concept. To get the optimized result in proposed skill‐based e‐learning, the fuzzy knowledge model is applied. In knowledge retrieval, the fuzzy membership value of the knowledge and the combinations of framed rules are used to acquire the knowledge. The fuzzy techniques are adapted for analyzing the retrieved knowledge concept of individual skills like Inadequate (Ia), Adequate (A), Value added adequate (Vaa), and Integrated skill (I) and in fuzzy inference system in skill‐based education provides a decision about the learner community skill set and it promotes the excellence skill through the delivery of the suitable courses to the learners instead of supplying common courses to different skilled persons. In the existing knowledge modeling methods known as the Knowledge Capturing and Modeling, concept map‐based knowledge modeling confirm the learners to have known or unknown domain concept. Further, many of the researchers present on the analysis of the learner performances, behavior, learning environment, etc. The proposed paper investigate the individual skill abilities and it is suggested a set of courses in adaptive curriculum and syllabus to the learner and also it adapts andragogy in skill‐based education, to model the fuzzy knowledge, the fuzzy membership function and rules are used.

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Abstract. Pervasive computing applications often entail continuous monitoring tasks, issuing persistent queries that return continuously updated views of the operational environment. We present PAQ, a middleware that supports applications' needs by approximating a persistent query as a sequence of one-time queries. PAQ introduces an integration strategy abstraction that allows composition of one-time query responses into streams representing sophisticated spatio-temporal phenomena of interest. A distinguishing feature of our middleware is the realization that the suitability of a persistent query's result is a function of the application's tolerance for accuracy weighed against the associated overhead costs. In PAQ, programmers can specify an inquiry strategy that dictates how information is gathered. Since network dynamics impact the suitability of a particular inquiry strategy, PAQ associates an introspection strategy with a persistent query, that evaluates the quality of the query's results. The result of introspection can trigger application-defined adaptation strategies that alter the nature of the query. PAQ's simple API makes developing adaptive querying systems easily realizable. We present the key abstractions, describe their implementations, and demonstrate the middleware's usefulness through application examples and evaluation.

Queries are convenient abstractions for the discovery of information and services, as they offer content-based information access. In distributed settings, query semantics are well-defined, for example, queries are often designed to satisfy ACID transactional properties. When query processing is introduced in a dynamic network setting, achieving transactional semantics becomes complex due to the open and unpredictable environment. In this article, we propose a query processing model for mobile ad hoc and sensor networks that is suitable for expressing a wide range of query semantics; the semantics differ in the degree of consistency with which query results reflect the state of the environment during query execution. We introduce several distinct notions of consistency and formally express them in our model. A practical and significant contribution of this article is a protocol for query processing that automatically assesses and adaptively provides an achievable degree of consistency given the operational environment throughout its execution. The protocol attaches an assessment of the achieved guarantee to returned query results, allowing precise reasoning about a query with a range of possible semantics. We evaluate the performance of this protocol and demonstrate the benefits accrued to applications through examples drawn from an industrial application.