Osteoporosis is a medical condition that affects the structure and strength of bones. Osteoporosis is an asymptomatic disease of the bone that affects a significant proportion of the world's elderly, leading to increased fragility of the bone and an increased risk of fracture. This paper's key objective is to provide a critical review of the main artificial intelligence-based systems for detecting populations at risk of osteoporosis or fractures. Skeletal deformities, fractures, twisted knees, inherited bone defects, and other bone disorders affect millions of individuals as a result of a variety of bone disorders. These may help to prevent a variety of possible complications if diagnosed and treated early. We discussed deep neural networks in this paper, including recognition, segmentation, and classification. The architecture and concepts of the deep learning algorithm we used to detect bone density were also discussed. As a result, well use a variety of deep learning algorithms to build a model that can detect a person's bone mass density and recognize any potential threats that have occurred or could occur.

Communication is very essential for all humans because it allows us to share or express our sentiments, emotions, and other thoughts. Humans communicate with one another via natural language (for example words), body language (hand gestures, facial motions, and so on) or writing etc. People who do not have any impairment can easily converse with each other in natural language. However people with impairments, such as deafness or blindness, suffer a communication hurdle. They converse with common people by adopting sign languages, which are difficult for normal people to grasp or understand. People with hearing and speech impairments often have a very difficult time conversing with other people without any translator or interpreter. As a matter of fact, this research is being undertaken that transforms sign language into text that is easily understood by the ordinary individual. This system will identify numbers, alphabets, and hand gestures as well. Our primary purpose is to remove the obstacles that exist between the deaf, dumb, and the rest of people.

The term "big data" refers to an information processing system that combines different conventional data techniques. Big data also includes a large amount of personally identifiable and authenticated data, making privacy a major concern. Various techniques have been developed to provide security and efficient data processing. Machine learning is a form of data technology that deals with one of the most important and least understood aspects of the data collected. Deep learning algorithms, similar to machine learning algorithms, learn programmers automatically from data and are thought to improve the efficiency and security of large data sets. The efficiency of machine learning and deep learning in a sensitive environment was evaluated in this paper, which reviewed security problems in big data. This paper begins by providing an overview of machine learning and deep learning algorithms. The research then moves on to machine learning problems and challenges, as well as potential solutions. The investigation into deep learning principles of big data continues after that. Finally, the report examines approaches used in recent research developments and concludes with recommendations for the future.