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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.