Attitude towards mental illness among doctors and nurses in a tertiary care centre, Pondicherry, India

Background: Stigma can prevent care and treatment of mentally ill. About 54% of diagnosable mental disorders are seen in primary care settings. There is a gross underestimation of psychiatric morbidity among patients by substantial proportion of non-psychiatric clinicians. Hence there is a need to assess the attitude towards mental illness among doctors and staff nurses. The objectives of the study were to assess the attitude towards mental illness among doctors and nurses, to compare the attitude between doctors and nurses, to find if there is any correlation between duration of training or posting and attitude, to find if educational status had any influence on attitude, to find if there is any gender influence on attitude. Methods: It is a cross sectional descriptive study conducted in a private medical college, Pondicherry among doctors and nurses who had completed their under graduation with a sample size of 221 (Doctors-120, Nurses-101). The instruments used were a semi-structured demographic profile and 34 items of OMICC (Opinion About Mental Illness in Chinese Community). The data was entered in Microsoft Excel 2013 analyzed using descriptive statistics, unpaired t-test, pearson’s correlation coefficient.Results: Only 25% of doctors and 4.9% of nurses positive attitude when overall score was considered. Doctors group had higher positive attitudes compared to nurses in domains separatism, stereotyping, benevolence and stigmatisation.Conclusions: There was no correlation between duration of psychiatry posting and attitude

An Intrusion Detection Using Machine Learning Algorithm Multi-Layer Perceptron (MlP): A Classification Enhancement in Wireless Sensor Network (WSN)

During several decades, there has been a meteoric rise in the development and use of cutting-edge technology. The Wireless Sensor Network (WSN) is a groundbreaking innovation that relies on a vast network of individual sensor nodes. The sensor nodes in the network are responsible for collecting data and uploading it to the cloud. When networks with little resources are deployed harshly and without regulation, security risks occur. Since the rate at which new information is being generated is increasing at an exponential rate, WSN communication has become the most challenging and complex aspect of the field. Therefore, WSNs are insecure because of this. With so much riding on WSN applications, accuracy in replies is paramount. Technology that can swiftly and continually analyse internet data streams is essential for spotting breaches and assaults. Without categorization, it is hard to simultaneously reduce processing time while maintaining a high level of detection accuracy. This paper proposed using a Multi-Layer Perceptron (MLP) to enhance the classification accuracy of a system. The proposed method utilises a feed-forward ANN model to generate a mapping for the training and testing datasets using backpropagation. Experiments are performed to determine how well the proposed MLP works. Then, the results are compared to those obtained by using the Hoeffding adaptive tree method and the Restricted Boltzmann Machine-based Clustered-Introduction Detection System. The proposed MLP achieves 98% accuracy, which is higher than the 96.33% achieved by the RBMC-IDS and the 97% accuracy achieved by the Hoeffding adaptive tree