Machine Learning and Artificial Intelligence in the Food Industry: A Sustainable Approach

Kler, Rajnish; Elkady, Ghada; Rane, Kantilal Pitambar; Singh, Abha; Hossain, Shamim; Malhotra, Dheeraj; Ray, Samrat; Bhatia, Komal Kumar

doi:10.1155/2022/8521236

Cited by 39 publications

(15 citation statements)

References 31 publications

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“…Due to the ongoing growth of machine learning (Hooda et al, 2022; Kler et al, 2022), the use of machine learning approaches to mine possible data knowledge has become a hot subject in the research of major insurance firms (Rawat et al, 2021). Using a database scraping tool, Ghasemaghaei et al (2018) reviewed a panel data set of consumer feedback for car, life and home insurance from January 2012 to December 2015.…”

Section: Literature Reviewmentioning

confidence: 99%

Customer Sentiment Analysis and Prediction of Insurance Products’ Reviews Using Machine Learning Approaches

Hossain

Rahman

2022

FIIB Business Review

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The study’s goal is to analyse and predict customer reviews of insurance products using various machine learning techniques. We gathered consumer rating data from the Yelp website and filtered the initial data set to only include insurance reviews. Following cleaning, the filtered summary texts were graded as positive, neutral or negative sentiments, and the AFINN and Valence Aware Dictionary for Sentiment Reasoning (VADER) sentiment algorithms were used to rate those sentiments. Furthermore, the current investigation employs five supervised machine learning approaches to divide customer ratings of insurance companies into three sentiment groups. The results of the current study revealed that the majority of customer reviews for the insurance products were negative, with the average number of words with negative sentiment being higher. In addition, current research discovered that while all of the approaches (decision tree, K Neighbours classifier, support vector machine (SVM), logistic regression and random forest classifier) can correctly classify review text into sentiment class, logistic regression outperforms in high accuracy. We analysed and predicted customer review messages using a variety of machine learning methods, which could help companies better understand how customers respond to their products and services. As a result, companies can learn how to use machine learning methods to better understand the behaviour of their customers.

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Section: Literature Reviewmentioning

confidence: 99%

Customer Sentiment Analysis and Prediction of Insurance Products’ Reviews Using Machine Learning Approaches

Hossain

Rahman

2022

FIIB Business Review

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“…Valorization of food wastes and by-products and exploitation of novel food sources take advantage of recent technological advances and the rapid spread of the concept of Industry 4.0. IoT, digital technologies, such as AI and digital twins, and other Industry 4.0 components are being applied to reduce or valorize food wastes and by-products, providing important environmental and economic benefits (62,(149)(150)(151)(152). For example, emerging innovations in the field of algae biotechnology enable the development of low-cost production with exciting opportunities of automation through the application of IoT and other technological advances (145).…”

Section: D-printed Foodsmentioning

confidence: 99%

Consumer acceptance of new food trends resulting from the fourth industrial revolution technologies: A narrative review of literature and future perspectives

Hassoun¹,

Cropotova

Trif

et al. 2022

Front. Nutr.

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The growing consumer awareness of climate change and the resulting food sustainability issues have led to an increasing adoption of several emerging food trends. Some of these trends have been strengthened by the emergence of the fourth industrial revolution (or Industry 4.0), and its innovations and technologies that have fundamentally reshaped and transformed current strategies and prospects for food production and consumption patterns. In this review a general overview of the industrial revolutions through a food perspective will be provided. Then, the current knowledge base regarding consumer acceptance of eight traditional animal-proteins alternatives (e.g., plant-based foods and insects) and more recent trends (e.g., cell-cultured meat and 3D-printed foods) will be updated. A special focus will be given to the impact of digital technologies and other food Industry 4.0 innovations on the shift toward greener, healthier, and more sustainable diets. Emerging food trends have promising potential to promote nutritious and sustainable alternatives to animal-based products. This literature narrative review showed that plant-based foods are the largest portion of alternative proteins but intensive research is being done with other sources (notably the insects and cell-cultured animal products). Recent technological advances are likely to have significant roles in enhancing sensory and nutritional properties, improving consumer perception of these emerging foods. Thus, consumer acceptance and consumption of new foods are predicted to continue growing, although more effort should be made to make these food products more convenient, nutritious, and affordable, and to market them to consumers positively emphasizing their safety and benefits.

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“…Industry 4.0 can be extensively deployed in functional aspects of business operations, establishing smart warehouses completely run by a set of autonomous intelligent platforms and devices such as automated guided vehicles (Tebaldi et al, 2021; Vukicevic et al, 2021). AI and ML can be used to optimise and streamline various aspects of the sales and distribution process, such as forecasting demand, determining pricing and identifying new markets (Kler et al, 2022). For example, AI‐powered analytics can help companies predict customer demand and optimise production and distribution accordingly (Kler et al, 2022).…”

Section: Implementation Of Industry 40 In CM Productionmentioning

confidence: 99%

Industry 4.0 technologies for cultivated meat manufacturing

Kamalapuram,

Choudhury

2024

Food Bioengineering

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Industry 4.0 integrates the physical, digital, and biological realms by applying digital automation in systems, processes, and manufacturing facilities. Industry 4.0 is actively shaping the development of intelligent food processing industries in cultivated meat (CM) sector. This integration plays a crucial role in accelerating progress within the global CM sector, facilitating the achievement of its objectives related to food sustainability, security, human health, environmental concerns, and hygiene. Incorporating Industry 4.0 into CM manufacturing systems empowers upstream and downstream production processes to become more intelligent and capable of self‐optimisation. However, enabling rapid adoption of Industry 4.0 by emerging startups and small to medium‐sized enterprises in the CM industry necessitates a thorough understanding of prerequisites and evaluation of technological and biological limitations. Challenges include the substantial initial costs associated with establishing Industry 4.0 infrastructure, robust cybersecurity measures to ensure effective risk management, and acquiring skilled professionals proficient in both operational and maintenance roles. Integrating Industry 4.0 with the evolving CM sector presents an exciting opportunity to foster business‐to‐business investments across various domains, including local markets, export opportunities, and the broader global consumer ecosystem.

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Machine Learning and Artificial Intelligence in the Food Industry: A Sustainable Approach

Cited by 39 publications

References 31 publications

Customer Sentiment Analysis and Prediction of Insurance Products’ Reviews Using Machine Learning Approaches

Customer Sentiment Analysis and Prediction of Insurance Products’ Reviews Using Machine Learning Approaches

Consumer acceptance of new food trends resulting from the fourth industrial revolution technologies: A narrative review of literature and future perspectives

Industry 4.0 technologies for cultivated meat manufacturing

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