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Depression, a major depressive disorder and anxiety are common medical illness which cause several symptoms that affect the way a person feels, thinks, and the way he/she acts. These disorders are not only hard to endure, but are also risk factors for heart disease, panic attacks, dementia, and thus causing severe distress and functional impairment.Overall, more than 50% of the general population in middle and high-income countries suffers from at least one of these mental disorder at some point in their lives. In order to detect these disorders at an early age, we have proposed a model that uses a standard psychological assessment and machine learning algorithms to diagnose the different levels of such mental disorders. In our proposed model we used five different types of AI algorithms: Convolutional neural network, Support vector machine, Linear discriminant analysis, K Nearest Neighbor Classifier and Linear Regression on the two datasets of anxiety and depression. These algorithms are used to find the severity level of anxiety and depression, a patient is going through. In this paper we compared the results of the five algorithms on the two datasets separately on the basis of different measurement metrics. The proposed model achieves the highest accuracy of 96% for anxiety and 96.8% for depression using the CNN algorithm while its results were compared with the other five algorithms we have used for our model.

Among different imaging techniques MRI, MRSI and CT scans are some of the widely use techniques to visualize brain structures to point out brain anomalies especially brain tumor. Identification of brain tumor accurately in clinical practices has always been a hard decision for neurologist as multiple exceptions might present in images which may lead dubious suggestion from neurologist.In our proposed model we are aiming towards brain tumor detection and 3d visualization of tumor more accurately in efficient way. Our proposed model composed of three stages such as classification of image using CNN whether any tumor exists of not; segmentation using multi thresholding to extract the detected tumor; and 3d visualization using polynomial interpolation. the proposed model enables enhancing the accuracy of tumor detection as compare to existing models as well as segmenting and 3d visualizing the detected tumor. we get 85% accuracy on our model comparing with others which is slightly more efficient in terms of classification and detection.

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Automated trading is used in most of the major markets of our world. In order to ensure sustainable development, incorporating ethical and socially responsible ideas while designing these Artificial Intelligence (AI) systems has become a necessity. Both the industry and the academia are working towards Responsible AI, which can make Socially Responsible Investments (SRI). This paper reviews the research on SRI investment in the financial sector and evaluates these methods, which can help find future research directions in Computational Finance. This survey looks at the machine learning techniques used for ethical decision-making while stock or forex trading, which will benefit any further research work on Responsible AI in Finance.<br>