Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains
Keywords:
potential, provides, opportunities, transferability, interconnectedAbstract
Deep Learning (DL) and Machine Learning (ML) techniques have been widely used in recent years to develop new and innovative products and services in various industries. These techniques have the potential to transform the way people think about and use technology. They have the capability to perform complex tasks and make accurate predictions. The efficiency of DL and ML algorithms has been studied in various domains, leading to significant progress in several applications. As technology and the domains become more interconnected, it is important to explore their effects on different sectors. One of the most important factors that can be considered when it comes to analyzing the generalizability and transferability of these techniques is cross-domain analysis. This allows us to identify the potential of these techniques to solve various problems. Cross-domain analysis is beneficial for several reasons. It allows us to identify ML and DL algorithms' limitations and strengths and transfer knowledge between them, which can help speed up the development of new solutions and decrease the time and effort involved in the process. If ML algorithm is able to perform high-accuracy in healthcare, it can provide valuable insights for the detection of financial fraud. For several reasons, cross-domain analysis is essential for the design and implementation of DL and ML algorithms. It helps in identifying the specific requirements and challenges of the given domain, and it enables the optimization of existing frameworks. The objectives and characteristics of each domain dictate the need for specific modifications or upgrades. This study aims to analyze the effects of DL and ML algorithms on different sectors, such as healthcare, financial services, and network security. It will examine the suitability and performance of different ML and DL algorithms in these domains. The findings of this research will allow us to gain a deeper understanding of their potential to address specific applications. The study covers the effects of DL and ML algorithms on different sectors, such as healthcare, NLP, financial services, and network security. It performs a comprehensive analysis of the different algorithms in these areas, including Gradient Boosting Machines (GBM), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM). DL algorithms, such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer are also evaluated for their suitability and performance. This research offers actionable insights to practitioners and researchers, guiding them in picking suitable algorithms for specific applications, ultimately serving the goals of network security, healthcare, financial services, and NLP. The findings of this study will contribute to the increasing number of people who know about the applications of DL and DL algorithms. It will also help practitioners and researchers use these tools effectively in various fields. The study's cross-domain analysis also provides opportunities to enhance and transfer knowledge.
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