**3.5 Financial fraud detection**

*Advances and Applications in Deep Learning*

*Image example of handwritten digits from the MNIST dataset.*

dialogue (**Figure 6**).

**Figure 5.**

**3.4 Games and robotics**

often by a large margin. ALBERT developed by Google is used to reduce the parameters via cross-layer parameters sharing. The state of the artwork in this domain is about multi-domain task-oriented dialogue system [14]. In 2020, it expected to combine common sense reasoning with language models, extending language model context to thousands of words and to have more focus on open-domain

Robots are the agents who are artificially intelligent and working in the realworld replacing humans. OpenAI and Dota 2 are popular games; in 2017, 1v1 bot beats top professional Dota 2 players; in 2018, OpenAI five lost two games against top Dota 2 player, while in 2019, OpenAI five beat OG team (the world champion in 2018). The OpenAI five win in 2019 is only because of the more training compute; the current version of OpenAI has consumed 800 petaflops/day and experiences about 45,000 years of dota self-play over 10 real-time months. The current version has 99.9%-win rate versus the 2018 version. It is one of the best experiences in deep learning that systems that learn to play with each other and incrementally

**10**

**Figure 6.**

*NLP and deep learning.*

Deep learning is playing a very important role in financial fraud detection. With the advent of technology and a significant amount of e-commerce platforms, the number of e-payments is increasing day by day chances of financial fraud, which is also a source of headache for banks and other financial institutions. Thus, focusing on fraud detection is a hot area of research. The author of [15] used auto-encoder for financial fraud detection [16]. This research uses deep learning model for fraud detection, while [17] proposed a solution to fraud detection using machine learning approach.

## **3.6 Deep learning in health-care**

In this modern era of computing, deep learning also produced best results medical and health care, that is, deep learning is used for cancer cell coordination, organ segmentation, protein folding, lesion detection, and image enhancement in the field of medicine. There are several other issues like [18–21] and much more where deep learning is directly involved in the suggestion of the ultimate solution to the problem in healthcare.

#### **3.7 Military**

Deep learning is used for making many different military devices used in wars or other spy services. The military is also working on robots to train the robots to handle the critical situation through these robots. The militaries of some countries are making their weapons more intelligent using AI. In a war zone, AI can be embedded in the robots for remote surgical support in healthcare.

#### **3.8 Cybersecurity**

Cybersecurity is also one of the hot research areas; deep learning models are used for the cybersecurity of the Internet of Things (IoT) [22]. The IoT devices are usually low power devices having power-constrained that's why always vulnerable to external threats. Deep learning models can detect threats more accurately than any other technology. The author of [23] used deep learning and machine learning for intrusion, spam, and malware detection.
