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Glaucoma is the 2d main purpose of everlasting blindness worldwide. Early detection of glaucoma can restrict the disorder development. This paper directs the exceptional picture processing strategies for glaucoma detection based totally on CDR. After calculating the effects are in contrast the usage of sensitivity and specificity. Glaucoma is a criticism prompted due to loss of feature of neurons of the eye which leads to loss of eye sight. The photo processing strategies are Multi level thresholding approach, model of Active Contour Method, Region Based Segmentation. It can be calculated by using gazing Intra optical pressure (IOP), view, optical slice appearance, Glaucoma influences the supplemental imaginative and prescient loss by using including Mug to disc ratio. [ABSTRACT FROM AUTHOR]

A system which recognizes hand poses and gestures from the Indian Sign Language (ISL) in real time using grid-based features is presented in this paper. An attempt is made to minimize the gap between the hearing and speech impaired and the rest of the people in the society. The solutions that are existing now neither provide relatively high accuracy nor do good work in real time. Good results on both these parameters are provided in this paper. Some gestures and 33 hand poses from the ISL can be identified using grid-based features. A smart phone camera is used to capture the sign language and are transmitted to remotely placed server in the form of frames. It is user friendly and external hardware usage is thus avoided. For hand detection and tracking, techniques such as skin color segmentation, face detection and object stabilization are used. Further to represent the hand's pose in the form of feature vector, the image grid-feature extraction technique is implemented on the image. K-nearest neighbors' algorithm is used to classify the hand poses. Hidden Markov Model chain is fed with the motion and observation sequence of intermediate hand poses to classify the gestures in which 12 gestures are predefined in ILS. [ABSTRACT FROM AUTHOR]

Social networking application platforms are experiencing considerable development in equally volume and principle. A detailed characteristic of Social networking application platforms is emotion investigation, whereby a person's emotions and feelings know how to be incidental from their posted text. Study connected to emotion examination is getting hold of important attention since it is a excellent organizer that be capable of progress client familiarity and make available innumerable modified services. Twitter is one of the largely accepted Social networking application platforms that encompass consumers commencing dissimilar counties by means of diverse traditions and verbal communications. It gives precious information for miscellaneous and huge quantity of information that is capable to be utilized to get better judgment building. In this document, the emotion direction of documented characteristics and emoji-based mechanisms is deliberated aiming "tweets" and observations posted in Arabic on Twitter for the duration of the 2018 World Cup occasion. The learning furthermore calculated the significance of analyzing texts together with or exclusive of emojis. Information was get hold of on or after thousands of tweets composed to discover emotion examination outcomes for texts and emojis independently. The outcomes demonstrate that emojis maintain the emotion direction of texts along with those texts or emojis balance each other to communicate proposed significance and consequently cannot offer dependable information independently. [ABSTRACT FROM AUTHOR]