Perspective Chapter: Podological Deformities and Its Management

The ankle and foot complex plays on important role in gait and weight bearing of the body weight. The deformity of the ankle and foot affects and alters the biomechanics of the body and normal gait pattern, and this consequently affects the other parts and joints of the lower limb and also trunk

A COMPARATIVE ANALYSIS OF WEB INFORMATION EXTRACTION TECHNIQUES DEEP LEARNING vs. NAÏVE BAYES vs. BACK PROPAGATION NEURAL NETWORKS IN WEB DOCUMENT EXTRACTION

Web mining related exploration is getting the chance to be more essential these days in view of the reason that a lot of information is overseen through the web. Web utilization is expanding in an uncontrolled way. A particular framework is required for controlling such extensive measure of information in the web space. Web mining is ordered into three noteworthy divisions: Web content mining, web usage mining and web structure mining. Tak-Lam Wong has proposed a web content mining methodology in the exploration with the aid of Bayesian Networks (BN). In their methodology, they were learning on separating the web data and characteristic revelation in view of the Bayesian approach. Roused from their investigation, we mean to propose a web content mining methodology, in view of a Deep Learning Algorithm. The Deep Learning Algorithm gives the interest over BN on the basis that BN is not considered in any learning architecture planning like to propose system. The main objective of this investigation is web document extraction utilizing different grouping algorithm and investigation. This work extricates the data from the web URL. This work shows three classification algorithms, Deep Learning Algorithm, Bayesian Algorithm and BPNN Algorithm. Deep Learning is a capable arrangement of strategies for learning in neural system which is connected like computer vision, speech recognition, and natural language processing and biometrics framework. Deep Learning is one of the simple classification technique and which is utilized for subset of extensive field furthermore Deep Learning has less time for classification. Naive Bayes classifiers are a group of basic probabilistic classifiers in view of applying Bayes hypothesis with concrete independence assumptions between the features. At that point the BPNN algorithm is utilized for classification. Initially training and testing dataset contains more URL. We extract the content presently from the dataset. The Three classification algorithm is utilized for the document extraction. The performance evaluation analyses the accuracy, review and F-measure values. The methodology gives a similar investigation of three algorithms with the performance evaluation for Deep Learning, Bayesian and BPNN Algorithm. There are considerable measures of methodologies that have been created in the zone of Web Information Extraction (IE), which concerns how to collect valuable data for further investigation from web pages

Enhancing human sight perceptions to optimize machine vision: Untangling object recognition using deep learning techniques

The goal of machine vision is to develop human-like visual abilities; however, it is unclear whether understanding human vision will advance machines. Here, it exemplifies two key conceptual advancements: It first shows that the majority of computer vision models consistently differ from the way that individuals perceive objects. To do this, a significant dataset of human perceptions of the separations of isolated things was acquired, and it was then examined to see if a well-known machine vision algorithm can predict these perceptions. The best algorithms can account for the majority of the volatility in the intuitive data, but every algorithm we verified repeatedly misjudged several different object types. Second, it shows that removing these systemic biases can considerably increase classification accuracy. For instance, machine techniques overestimated detachments between symmetric objects in comparison to human vision. These results illustration that methodical differences between human and machine vision can be identified and improved.In order to improve the machine vision, employing a deep learning algorithm Visual Geometry Group (VGG 16) with planar reflection symmetry (PRS-Net) technique. VGG 16 is a convolutional neural network with 16 deep layers. VGG pre-trained architecture can point out visual features present in the image. The planar reflection symmetry concept is appended with VGG to create a hybrid environment that can improve machine vision significantly by 90%