As technology advances daily, are advancing our lives into the digital sphere. The introduction of these cryptocurrencies aims to prevent the financial crisis. Due to its decentralized nature, high level of security, and restrictions on the number of coins that may be created, cryptocurrencies have attracted investors. Predicting the future price includes several limits and determinants because it involves capital. It varies according to market share. Using block chain technology and encryption, the transactions are encrypted from the beginning to end. Predicting prices to encourage consumers to invest during a specific period and earn a profit. They include a variety of elements, such as market analysis, sentiment analysis on Twitter, trading volume, and open and closing prices. Typical models that can be used to forecast bitcoin prices include regression techniques, neural networks, and support vector machines. Predictions of cryptocurrency prices based on their closing prices give investors additional insight into whether to wait until the closing period if prices are low for the entire day or to invest the following day. Using deep learning and bidirectional Long Short-Term Memory (LSTM) suggested this model to anticipate the price of digital currencies including Bitcoin, Litecoin, Ethereum, and Cardano. In this model, predictions are made using historical price statistics, and the graph is created by evaluating several performance indicators.

In today's fast-paced society, psychological health issues such as anxiety, depression, and stress have become prevalent among the general population. Researchers have explored the use of machine learning algorithms to predict the likelihood of depression in individuals. As datasets related to depression become more abundant and machine learning technology advances, there is an opportunity to develop intelligent systems capable of identifying symptoms of depression in written material. By applying natural language processing and machine learning algorithms to analyze written text, such as social media posts, emails, and chat messages, researchers can potentially identify patterns and linguistic cues associated with depression. These patterns may include changes in word usage, tone, and sentiment. The dataset consists of text-based questions on this information channel. At present, machine learning techniques are highly effective for analyzing data and identifying problems. Researchers have conducted comparisons of the accuracy achieved by different machine learning algorithms using the complete set of attributes as well as a subset of selected attributes. In summary, while the potential for AI to aid in mental health diagnosis and treatment is exciting, it's important to proceed with care and consideration for the complexities of the field and the needs of patients.

People with limited vision, impaired sight, or visual impairment cannot see or recognize people, objects, words, or letters. Offer visually challenged people a camera-based detection system so they can read names of trained people, products, objects, and texts. With the aid of facial recognition technology, the suggested system will enable them to recognize a person and some of the items in front of them, including (Bottle, chair, person, cat, dog, plant). The printed text on books, magazines, and other printed materials can be read by employing optical character recognition technology. The proposed technology serves as a substitute for an artificial eye for persons who are visually impaired. It doesn’t require any oversight from people.

Emotions are the best indicators of the actions of humans in advance. It is of great advantage in the current smart world. Prediction of these emotions can be able to sense the current mood of the driver and control the smart automobile accordingly and can be used in the case of chatting with customers using AI devices. This can be done by extracting the features including Mel-frequency cepstral coefficients (MFCCs) of the respective emotions and training the learning model using the classification algorithm Convolutional Neural Networks (CNN) and eventually the model can predict the emotion by comparing the newly retrieved features and the features of the training dataset and classify them accordingly.