In recent years, digital marketing has surpassed traditional marketing as the preferred technique of reaching customers. Researchers and academics may utilize it for social media marketing and for predicting client buy intent, among other applications. It can boost customer happiness and sales by facilitating a more personalized shopping session, resulting in higher conversion rates and a competitive advantage for the retailer. Advanced analytics technologies are utilized in conjunction with a dynamic and data-driven framework to expect whether or not a customer will make a purchase from the organization within a certain time frame. To increase income and stay ahead of the competition, one must understand customer buying habits. Several sectors offered rules to explore a consumer's potential based on statistics results. A machine learning algorithm for detecting potential customers for a retail superstore is proposed using an engineering approach.

The act of digital marketing uses a variety of traditional methods such as analyst consensus, earnings per share estimation, or fundamental intrinsic valuation. Also, social media management, automation, content marketing, and community development are some of the most popular uses for digital marketing. Stock price prediction is a challenging task since there are so many factors to take into account, such as economic conditions, political events, and other environmental elements that might influence the stock price. Due to these considerations, determining the dependency of a single factor on future pricing and patterns is challenging. The authors examine Apple's stock data from Yahoo API and use sentiment categorization to predict its future stock movement and to find the impact of “public sentiment” on “market trends.” The main purpose of this chapter is to predict the rise and fall with high accuracy degrees. The authors use an artificial intelligence-based machine learning model to train, evaluate, and improve the performance of digital marketing strategies.

No abstract

Underwater Fish Species Recognition (UFSR) has attained significance because of evolving research in underwater life. Manual techniques to distinguish fish can be tricky and tedious. They might require enormous inspecting endeavours, but they can be costly. It results in limited data and a lack of human resources, which may cause incorrect object identification. Automating the fish species detection and recognition utilizing technology would assist sea life science to evolve further. UFSR in wild natural habitats is difficult because the images open natural habitat, complex background, and low luminance. Species Visualization can assist us with deep knowledge of the movements of the species underwater. Automation systems can help to classify the fish accurately and consistently. Image classification has been emerging research with the advancement of deep learning systems. The reason is that the convolutional neural networks (CNNs) don't require explicit feature extraction methods. The vast majority of the current object detection and recognition mechanisms are based on images in the outdoor environment. This paper mainly reviews the strategies proposed in the past years for underwater fish detection and classification. Further, the paper also presents the classification of three different underwater datasets using CNN with evaluation metrics.