Monika Lamba

Tourism marketers can examine their traveler's brain waves and how different stimuli affect them through using neuroscience in tourism, also referred as “neuro-tourism”. Neuro-tourism allows access to travelers' minds, giving us real-time insight into what they consciously and unconsciously feel. Neuromarketing methods such as brain–computer interface (BCI), eye tracking, galvanic skin reaction have been implemented in neuro-tourism for purposes of designing tourism products and services that increase visitors happiness and experience. Since neuro-tourism is relatively new and there are few studies on the topic, this chapter offered an indepth investigation of peer reviewed journal papers published within neuro-tourism research during the past years in order to highlight possible trends in this line of research and present implications for researchers.

Environmental Social Governance (ESG) merged with Digital Transformation has become one of the hottest research areas influenced by sustainability, technology, and business requirements. This paper aims to provide a comprehensive overview of ESG correlated with digital transformation through a systematic literature review and bibliometric analysis. Searches were conducted on the Scopus database using keywords associated with ESG, sustainability, AI, Industry 4.0, and digital keywords. Initially, 919 publications were found; after screening and eligibility assessment, 43 studies indexed in Scopus were included for final analysis. For bibliometric analysis, a three-stage thematic protocol consisting of assembling, arranging, and assessing was followed. Bibliometric analysis was performed by using Microsoft Excel, Bibliometrix (R Studio), and VOSviewer to demonstrate publication patterns, research areas, keywords with cluster analysis, research themes over time, and country-wise contribution to research.

The goal of this chapter is to fill in the blanks about the purpose, significance, and potential of combining eye tracking study with AI. The main purpose of the work was to come up with a plan for how eye tracking and AI could be used together to improve marketing communication management. Our study used a structured literature review, online content analysis of eye tracking studies. Through the use of sensors and cameras that track the position and movement of the eyes while they gaze at an object, eye tracking technology helps to illustrate what is happening in a user interface by seeing and recording the movements of the subject's eyes. The system flashes infrared light into the eyes, which causes reflections on the pupil and cornea. Eye-tracking sensors record these reflections, process the data, and use algorithms to determine the user's gaze. The point of gaze provides insight into what is capturing and maintaining individual's attention by revealing where they stare on a screen or the surrounding area. As researchers and designers, this technology aids in our comprehension of how people perceive and absorb information. These days, neuromarketing is a relatively young field that is crucial to consumer research and marketing. Businesses thrive, particularly in the business world, on gaining a deeper understanding of the attitudes, emotions, and uncontrollable reactions of their potential clients. In these situations, it is highly beneficial for them to explore using AI in neuromarketing technologies as a study strategy. The findings of an eye-tracking experiment that examines how consumers respond to home service robots and their look are presented in this chapter.

Cassava, a widely cultivated staple food crop in the tropics, is frequently afflicted by various diseases that significantly decrease its yield. Cassava leaf disease diagnosis with four common cassava leaf diseases: cassava brown streak disease, cassava green mite, cassava bacterial blight, and cassava mosaic disease using state-of-the-art image classification techniques of deep CNN was implemented. In the proposed method data augmentation approaches have been applied to enlarge the dataset for better model performance. The methodology uses machine learning methods to surpass the conventional manual identification methods for recognizing affected leaf images, potentially increasing agricultural productivity through faster and more accurate disease detection that will lead to improved yield and better resource management in cassava cultivation. Evaluated the performance of various Deep Learning architectures, including LeNet, VGG-16, ResNet-50 and Efficient Net, as well as used various callback techniques. These include early stopping to prevent overfitting and model checkpointing, which saves the best-performing model, along with a learning rate reduction for fine-tuning these models and further optimizing their performance. The research, therefore, opens the possibilities of automated disease detection, greatly helping farmers in timely and accurate diagnosis for better management practices to ensure increased food security in cassava-growing regions, especially when mobile technology increases access to diagnostic tools and supports the field applicability of machine learning models. Further, deploying such models in the field has another advantage in embedding disease surveillance into the day-to-day activities of farmers, which again becomes an effective way to improve crop management strategies and responses in different agricultural contexts.