We encountered an extremely rare case where a patient with cat eye syndrome (CES) who presented with symptoms of posterior semicircular canal dehiscence (PSCD). CES is a rare genetic disorder, resulting from duplication of chromosome 22. Patients may present with variable phenotypes, including characteristic of coloboma, heart defect, periauricular skin pit/tag, microtia, anal atresia and mildly retarded mental development in some cases. PSCD is also a disease of the inner ear, where patients present with third window signs and symptoms due to lack of bony coverage. PSCD is usually associated with a high riding jugular bulb and fibrous dysplasia. In this study, we report a new otologic finding in CES patient as an association of PSCD and high jugular pulp. We describe the work up and its findings and the management of this patient.SIMILAR CASES PUBLISHED:: None.

Today growing number of corporations and research groups can rely on this new tool for are hitching up artificial intelligence horsepower. Insurance, Banking, Retail, Telecom and many other such sectors can find it fruitful for optimizing their options. artificial intelligence applications are becoming more prevalent: the improving tax collections and detecting tax fraud; improving its health care for employees while reducing the corporation’s costs; We are beginning to figure out how to mine these growing mountains of artificial intelligence, data, and parallelism makes the mining operations possible. We need to provide efficient solution to solve data clustering in GPU. Hierarchical parallel processing method is applied to find the data clusters in GPU. Deep nearest neighbor searching algorithm is used to create deep belief network and predict the accuracy. The efficiency is determined in the training set using the mean square error rate. The obtained results are compared with the traditional techniques. The result is tested by using TensorFlow using different GPU time slots.

Sparse secret writing, primarily based on abnormal detection, has shown promising performance, key features being feature learning, subtle illustrations, and vocabulary learning. propose a replacement neural network for anomaly detection called AnomalyNet by deep feature learning, sparse representation, and dictionary learning in 3 collaborative neural processing units. In particular, to obtain higher functions, form the motion fusion block in the middle of the function transfer block to enjoy the benefits of eliminating background noise, motion capture, and eliminating information deficit. In addition, to deal with some of the shortcomings (such as non-adaptive updating) of existing sparse coding optimizers and to take advantage of the advantages of neural network (such as parallel computation), design a unique continuous neural network, which will be told as a thin illustration of a docent dictionary by proposing a consistent iterative rule of hard threshold (adaptive ISTA) and the reformulation of adaptive ISTA as a substitute for long-term memory (LSTM). As far as we know, this may be one of the first works to link the `1-solvers and LSTM and offer new insights into LSTM and model-based refinement (or so-called differential programming), but primarily in the form of detection-based sparse secret writing anomaly. In-depth, experiments show the progressive performance of our technique in the task of detecting abnormal events.

Chronic kidney disease (CKD) is still a health concern, even though surgical care and treatment have improved. Recently, academics throughout the globe have been more interested in creating high-performance approaches for diagnosing, treating, and preventing kidney disease by being more knowledgeable about the aspects that the issue is concerned with, designed to provide better. Evaluation of patient records for patients may assist health care providers detects sickness earlier on. Several have tried to construct sophisticated algorithms that forecast CKD by analyzing health data, but their effectiveness needs improvement. An intelligence categorization and linear regression model are suggested in this paper. The kidney-related disorders are predicted using a customized stacked dense network model (). Compared to current models, the testing of the conceptual scheme reveals that it can predict CKD with 98.5% accuracy. Research suggests that utilizing sophisticated deep learning algorithms is advantageous for treatment decisions and may assist in the early diagnosis of CKD and its associated stages, reducing the development of kidney problems.

Machine learning is the subset of Artificial Intelligence and it is used for prediction various real time data analytics applications. Health care monitoring is the major area to analyse the result and make effective decisions. We need intelligent and automated process for predicting diseases using medical dataset. Machine learning methods are proposed to handle the dataset. Smart healthcare prediction is proposed to identify the user or patient information or symptoms as an input. Our system has forecasting accuracy index based on likelihood of the disease and health information. We use Naive bayes classifier algorithm for handling classification, prediction and accuracy index of dataset. Our algorithm measures the disease percentage and train the dataset. Once the prediction result will appears based on effective decision to be taken. In our work, we are taken 20000 train dataset and 7500 test data set for evaluation. TensorFlow simulator is used to simulate the system and measure accuracy. In this system achieves 95% accuracy and performance result compared with existing methods.