Comprising the largest population cohort on this planet, Gen Z presents a future-oriented consumer segment driven by climate change and food. This study sought to investigate Gen Z’s perceptions toward plant-based foods and diets and explore the relationship that attitude components, meal preparation involvement, personal and lifestyle factors, and perceived barriers in adopting a plant-based diet have with willingness to adopt green-eating practices. Using cross-sectional data from university students in Greece, India, and the UK, various tools were employed to determine the factors influencing youths’ consumer behavior toward animal-protein substitutes. PCA indicated the underlying dimensions of students’ viewpoints on plant-based foods, whereas hierarchical and k-means clustering provided the cluster structure. An ordered probit model was estimated to delineate Gen Z’s willingness to adopt plant-based diets and distinguish among mostly unwilling, somewhat willing, and mostly willing youths. Our findings identified two consumer segments, namely proponents and opponents of plant-based foods and diets, with statistically significant differences in the perceived health benefits of plant-based diets, attachment to animal-based proteins, perceived exclusion of animal-based foods, dissatisfaction with plant-based foods’ attributes, and demand for ensuring adequate protein intake. The ordered probit model estimates showed that there is a “homogeneity” in the factors influencing youths’ intention to adopt plant-based diets, with attitude components, meal preparation indicators, perceived barriers to eating “green”, and personal factors, such as self-assessed knowledge of healthy eating and physical activity, being strongly associated with students’ willingness to switch to plant-based diets in all three countries. Mapping potential obstacles and enablers in terms of shifting to more green-eating behaviors, our findings could add information to better understand the factors affecting food choice and youths’ transition to a more sustainable lifestyle.

The human genome has a profound impact on human health and disease detection. Carcinoma (cancer) is one of the prominent diseases that majorly affect human health and requires the development of different treatment strategies and targeted therapies based on effective disease detection. Therefore, our research aims to identify biomarkers associated with distinct cancer types (gastric, lung, and breast) using machine learning. In the current study, we have analyzed the human genomic data of gastric cancer, breast cancer, and lung cancer patients using XGB-BIF (i.e., XGBoost-Driven Biomarker Identification Framework for detecting cancer). The proposed framework utilizes feature selection via XGBoost (eXtreme Gradient Boosting), which captures feature interactions efficiently and takes care of the non-linear effects in the genomic data. The research progressed by training XGBoost on the full dataset, ranking the features based on the Gain measure (importance), followed by the classification phase, which employed support vector machines (SVM), logistic regression (LR), and random forest (RF) models for classifying cancer-diseased and non-diseased states. To ensure interpretability and transparency, we also applied SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), enabling the identification of high-impact biomarkers contributing to risk stratification. Biomarker significance is discussed primarily via pathway enrichment and by studying survival analysis (Kaplan–Meier curves, Cox regression) for identified biomarkers to strengthen translational value. Our models achieved high predictive performance, with an accuracy of more than 90%, to classify and link genomic data into diseased (cancer) and non-diseased states. Furthermore, we evaluated the models using Cohen’s Kappa statistic, which confirmed strong agreement between predicted and actual risk categories, with Kappa scores ranging from 0.80 to 0.99. Our proposed framework also achieved strong predictions on the METABRIC dataset during external validation, attaining an AUC-ROC of 93%, accuracy of 0.79%, and Kappa of 74%. Through extensive experimentation, XGB-BIF identified the top biomarker genes for different cancer datasets (gastric, lung, and breast). CBX2, CLDN1, SDC2, PGF, FOXS1, ADAMTS18, POLR1B, and PYCR3 were identified as important biomarkers to identify diseased and non-diseased states of gastric cancer; CAVIN2, ADAMTS5, SCARA5, CD300LG, and GIPC2 were identified as important biomarkers for breast cancer; and CLDN18, MYBL2, ASPA, AQP4, FOLR1, and SLC39A8 were identified as important biomarkers for lung cancer. XGB-BIF could be utilized for identifying biomarkers of different cancer types using genetic data, which can further help clinicians in developing targeted therapies for cancer patients.

Comprising the largest population cohort on this planet, Gen Z presents a future-oriented consumer segment driven by climate change and food. This study sought to investigate Gen Zs’ perceptions to-ward plant-based foods and diets, and explore the relationship between attitude components, meal preparation involvement, personal and lifestyle factors, and perceived barriers in adopting a plant-based diet with willingness to adopt green-eating practices. Using cross-sectional data from uni-versity students in Greece, India and UK, various tools were employed to determine the factors influ-encing youths’ consumer behavior toward animal-protein substitutes. PCA indicated the underlying dimensions of students’ viewpoints on plant-based foods, whereas hierarchical and k-means clustering provided the cluster structure. An ordered probit model was estimated to delineate Gen Zs’ willingness to adopt plant-based diets and distinguish among mostly unwilling, somewhat willing and mostly willing youths. Our findings identified two consumer segments, namely proponents and opponents toward plant-based foods and diets, with statistically significant differences in the perceived health benefits of plant-based diets, attachment to animal-based proteins, perceived exclusion of ani-mal-based foods, dissatisfaction with plant-based foods attributes and demand for ensuring adequate protein intake. The ordered probit model estimates showed that there is a rather «homogeneity» in the factors influencing youths’ intention to adopt plant-based diets, with attitude components, meal prep-aration indicators, perceived barriers to eating “green” and personal factors, viz. self-assessed knowledge on healthy eating and physical activity, being strongly associated with students’ willingness to switch to plant-based diets in all three countries. Mapping potential obstacles and enablers in terms of shifting to more green-eating behaviors, our findings could add information to better understand the factors affecting food choice and youths’ transition to a more sustainable lifestyle.

Conversational AI has seen unprecedented growth in recent years due to which Chatbots have been made available. Conversational AI primarily focuses on text or speech inputs, identifying the intention behind them, and responding to users with relevant information. Natural Language Processing (NLP), Natural Language Understanding (NLU), Machine Learning (ML), and speech recognition offer a personalized experience that mimics human-like engagement in conversational AI systems. Conversational AI systems like Google Meena, Amazon’s Alexa, Facebook’s BlenderBot, and OpenAI’s GPT-3 are trained using Deep Learning (DL) techniques that mimic a human brain-like structure and are trained on huge amounts of text data to provide open-domain conversations. The aim of this chapter is to highlight Conversational AI and NLP techniques behind it. The chapter focuses on DL architectures useful in building Conversational AI systems. The chapter discusses what are the recent advances in Conversational AI and how they are useful, what are the challenges, and what is the scope and future of conversational AI. This will help researchers to understand state-of-the-art frameworks and how they are useful in building Conversational AI models.

Sustainable Agriculture is rapidly emerging as an important discipline to meet societal needs for food and other resources by adopting paradigms of conserving natural resources while maximizing productivity benefits. This paper proposes an integrative methodological approach for critically analyzing Precision Farming (PF) paradigms and Zero Budget Natural Farming (ZBNF), providing sustainable farming solutions and achieving productivity and profitability. This paper analyses the productivity of crops in PF using various machine learning (ML) algorithms based on different soil and climatic factors to identify sustainable agricultural practices for maximizing crop production and generating recommendations for the farmers. When implemented on the collected dataset from various Indian states, the Random Forest (RF) model produced the best results with an AUC-ROC of 95.7%. The Juxtaposition of ZBNF and non-ZBNF is evinced. ZBNF is statistically (p<0.05) observed to be a cost-efficient and more profitable alternative. The impact of ZBNF on soil microbial diversity and micro-nutrients is also discussed.

Food wastage is a complex social, economic, and environmental issue worldwide. Approximately one-third of edible food products are wasted globally. There is an urgent need to raise awareness about food wastage. The Theory of Planned Behavior (TPB), which encompasses attitude, subjective norm, and perceived behavioral control, has been extensively used to study food-related behaviors. In this study, we propose an extension of TPB by adding three attributes: self-responsibility, eco-consciousness, and food and home management, aiming to develop a more comprehensive framework for predicting food wastage behavior. A cross-sectional survey was conducted among individuals of different age groups, measuring the impact of various attributes on food wastage. A total of 341 responses were collected across India. The data were analyzed using statistical methods and machine learning (ML) algorithms such as Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). The prediction accuracy of these ML classifiers for TPB and its extension was compared. Additionally, explainable artificial intelligence (XAI) techniques were employed to assess feature importance and identify significant attributes influencing food wastage. Results indicated that the prediction accuracy of all ML classifiers improved by 12% after incorporating self-responsibility, eco-consciousness, and food and home management attributes, thereby validating the extension of TPB. The integration of XAI highlighted key attributes associated with food wastage and increased the transparency of complex predictive models.