مسؤولية المجتمع في معالجة البطالة على سبيل الوجوب في ضوء السنة

The phenomenon of unemployment is one of the problems, which affects the development of individuals and society. Total unemployment or underemployment may be permanent or temporary. Its negative and damaging effects lay an everlasting result especially in times of economic recession. The importance of this study is to explore the role of individual, and methods of solution in the light of Sunnah. Hadith and Sunnah clearly mark the virtues of work and its value and positive impact on the community. Thus we see the greatness of our religion in this concern for human beings and preserving their dignity, and to find ways to ensure decent life, where there is neither no vacuum, nor unemployment.

Prediction of Financial commodites using AI

Stock market prediction has been an area of interest for a few decades now and much recently, there has been a lot of new research in the field of neural networks and natural language processing (NLP), that has resulted in satisfactory results. Sentiment analysis is one such sub-field of NLP that has made possible the extraction of emotions expressed in a body of text. These sentiments have been shown to co-relate with the change in stock prices. Previously twitter has been used to show a positive co-relation between the positive and negative sentiments gathered from a collection of tweets, and directional stock movements. In this paper, I expand upon these findings as I develop multiple predictive models that incorporates these technologies to predict not only the directional changes in the stock market but also the stock prices. For this purpose I use New York Times article headlines for the top 5 IT corporations listed on the S&P 500 index (Google, Microsoft, Amazon, Facebook and Apple), and use word2vec for encoding these headlines into vectors. These vectors are fed into LSTM NN, along with momentum-based economic indicators to supplement the predictive model. This has resulted in a MAPE (Mean Absolute Percentage Error) score of 1.15% for the regression based model and 65% direction accuracy, hence, indicating a positive co-relation between stock prices and public sentiment