scholarly journals Opportunities of Artificial Intelligence and Machine Learning in the Food Industry

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Indrajeet Kumar ◽  
Jyoti Rawat ◽  
Noor Mohd ◽  
Shahnawaz Husain
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Supply Chain ◽  
Food Safety ◽  
Food Industry ◽  
Human Error ◽  
Data Science ◽  
Food Industries ◽  
Sales Prediction ◽  
Supply Chain Management System

The food processing and handling industry is the most significant business among the various manufacturing industries in the entire world that subsidize the highest employability. The human workforce plays an essential role in the smooth execution of the production and packaging of food products. Due to the involvement of humans, the food industries are failing to maintain the demand-supply chain and also lacking in food safety. To overcome these issues of food industries, industrial automation is the best possible solution. Automation is completely based on artificial intelligence (AI) or machine learning (ML) or deep learning (DL) algorithms. By using the AI-based system, food production and delivery processes can be efficiently handled and also enhance the operational competence. This article is going to explain the AI applications in the food industry which recommends a huge amount of capital saving with maximizing resource utilization by reducing human error. Artificial intelligence with data science can improve the quality of restaurants, cafes, online delivery food chains, hotels, and food outlets by increasing production utilizing different fitting algorithms for sales prediction. AI could significantly improve packaging, increasing shelf life, a combination of the menu by using AI algorithms, and food safety by making a more transparent supply chain management system. With the help of AI and ML, the future of food industries is completely based on smart farming, robotic farming, and drones.

scholarly journals Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future

2021 ◽  
Vol 13 ◽  
pp. 175628722110448
Author(s):  
B.M. Zeeshan Hameed ◽  
Gayathri Prerepa ◽  
Vathsala Patil ◽  
Pranav Shekhar ◽  
Syed Zahid Raza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Error ◽  
Data Science ◽  
Image Interpretation ◽  
X Ray ◽  
X Ray Imaging ◽  
Potential Applications ◽  
Efficiency And Productivity

Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, and is time-consuming. To improve efficiency and productivity, incorporating machine learning (ML) and deep learning (DL) algorithms could expedite the process. This article aims to review the role of artificial intelligence (AI) and its contribution to data science as well as various learning algorithms in radiology. We will analyze and explore the potential applications in image interpretation and radiological advances for AI. Furthermore, we will discuss the usage, methodology implemented, future of these concepts in radiology, and their limitations and challenges.

A Comparison of Rule-Based and Machine Learning Models for Classification of Human Factors Aviation Safety Event Reports

2020 ◽  
Vol 64 (1) ◽  
pp. 129-133
Author(s):  
Katherine Darveau ◽  
Daniel Hannon ◽  
Chad Foster
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Human Error ◽  
Data Science ◽  
Aircraft Engine ◽  
Rule Based ◽  
Root Cause ◽  
Textual Data ◽  
Safety Event

There is growing interest in the study and practice of applying data science (DS) and machine learning (ML) to automate decision making in safety-critical industries. As an alternative or augmentation to human review, there are opportunities to explore these methods for classifying aviation operational events by root cause. This study seeks to apply a thoughtful approach to design, compare, and combine rule-based and ML techniques to classify events caused by human error in aircraft/engine assembly, maintenance or operation. Event reports contain a combination of continuous parameters, unstructured text entries, and categorical selections. A Human Factors approach to classifier development prioritizes the evaluation of distinct data features and entry methods to improve modeling. Findings, including the performance of tested models, led to recommendations for the design of textual data collection systems and classification approaches.

Data Science: Big Data, Machine Learning, and Artificial Intelligence

2018 ◽  
Vol 15 (3) ◽  
pp. 497-498 ◽  
Author(s):  
Ruth C. Carlos ◽  
Charles E. Kahn ◽  
Safwan Halabi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Science

Exploration of COVID-19 impact on the dimensions of food safety and security: a perspective of societal issues with relief measures

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Janpriy Sharma ◽  
Mohit Tyagi ◽  
Arvind Bhardwaj
Keyword(s):  
Supply Chain ◽  
Food Safety ◽  
Agricultural Practices ◽  
Research Literature ◽  
Content Type ◽  
Food Industries ◽  
Societal Issues ◽  
The Past ◽  
The Government ◽  
The Impact

PurposeOutbreak of the COVID-19 pandemic has created the catastrophic situation, it has crippled all the economic activities and seized off the operations of food supply chain (FSC). Disrupted FSC escalated the societal concerns related to food safety and security. The purpose of this study is to consolidate various issues, exploring the perspectives associated with the agricultural practices, food industries and society concerns related with the FSC performance system dynamics amid of COVID-19 pandemic.Design/methodology/approachTo structure this work, a detailed research literature insight focussing on the key findings associated with the past disease outbreaks like influenza, avian flu, Ebola, bird flu, SARS, foot and mouth disease and ongoing phase of COVID-19, encompassing the perspective related with various agricultural and concerned supply chain practices is clustered. Furthermore, issues having relevancy with the notion of this work, sourced from platforms of print and electronic media have been incorporated to ground the reality associated with the impacts, for better visualisation of the perspectives.FindingsThis study outlays the key findings which are relevant with the past pandemic outbreaks from the core of the research literature. It details the impact of the current COVID-19 scenario on the various FSC operations, focussing on dimensions allied with the industry, economic and society concerns. For the same, to mitigate the effects, relief measures focussing on the short- and long-term perspectives have been incorporated. Steps ramped up by the Government of India (GOI) to safeguard masses from the threat of food security, accelerate pace of the FSC operations and upscale operating capacities of the industries and agriculture practices have incorporated.Research limitations/implicationsPresented work is persuaded amid of the COVID-19 lockdown restrictions hence it outlays the theoretical perspectives only. But, these perspectives portray the ongoing scenario's impacts, extending its implication to the people coming from the industry and academia background. This study can felicitate the government bodies to make them familiar with the various impacts which indented the FSCs, food industries and added woes to the society concerns.Originality/valueIndia is the second largest populated nation of the world, and outspread of the COVID-19 has capsized the FSCs and raised the various instances, making population vulnerable to the threats of food insecurity. This study encompasses effect of the FSC disruption by incorporating its effect on the food industries practices, societal issues and extending possible relief measures to restructure the FSC dynamics. As of now, study focussing on the Indian FSC concerns, detailing of impacts due to pandemic outbreak, relief measures to sail out of the hard times are not available.

Artificial Intelligence, Machine Learning, and Data Science Technologies

2021 ◽  
Author(s):  
Neeraj Mohan ◽  
Ruchi Singla ◽  
Priyanka Kaushal ◽  
Seifedine Kadry
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Science

Artificial Intelligence, Machine Learning and Data Science as Iterations of Business Automation for Small Businesses

2020 ◽  
pp. 87-94
Author(s):  
Pooja Sharma ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Multinational Corporations ◽  
Small Businesses ◽  
Data Science ◽  
Small Data ◽  
Decision Systems ◽  
Business Operations ◽  
Business Automation ◽  
Machine Learning Tool

Artificial intelligence and machine learning, the two iterations of automation are based on the data, small or large. The larger the data, the more effective an AI or machine learning tool will be. The opposite holds the opposite iteration. With a larger pool of data, the large businesses and multinational corporations have effectively been building, developing and adopting refined AI and machine learning based decision systems. The contention of this chapter is to explore if the small businesses with small data in hands are well-off to use and adopt AI and machine learning based tools for their day to day business operations.

Investigating Relationships Between Supply Chain Capabilities, Competitive Advantage, and Business Performance

2018 ◽  
pp. 477-503
Author(s):  
Navee Chiadamrong ◽  
Tran Thi Tham
Keyword(s):  
Supply Chain ◽  
Structural Equation ◽  
Business Performance ◽  
Food Industry ◽  
Critical Factor ◽  
Business Environment ◽  
Equation Modeling ◽  
Competitive Advantages ◽  
Manufacturing Companies ◽  
Food Industries

Growing in the competitive environment, organizations need to find ways to improve their performance even better by ensuring that all key drivers are being developed and utilized effectively. Thai and Vietnamese food industries are rapidly growing sectors. This study investigates the relationships between supply chain capabilities and competitive advantages towards business performance, and compares the above mentioned relationships between Thai and Vietnamese food industries. The data were gathered from conducted surveys with the food manufacturing companies in both countries, and tested by Structural Equation Modeling. The empirical results show that supply chain capabilities play an important role in business improvement in both countries. While, supply chain integration is considered as a critical factor for the Vietnamese food industry, human resource management is much regarded as important for the Thai food industry. These findings help companies in each country decide the best strategy for differentiating themselves in their business environment.

scholarly journals Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Beverages ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 62 ◽  
Author(s):  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Frank R. Dunshea ◽  
Sigfredo Fuentes
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Food Industry ◽  
Emerging Technologies ◽  
Low Cost ◽  
Consumer Preference ◽  
Beverage Industry ◽  
Wide Range ◽  
Alcoholic Drinks ◽  
Different Levels

Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.

scholarly journals Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 193 ◽  
Author(s):  
Sebastian Raschka ◽  
Joshua Patterson ◽  
Corey Nolet
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Science ◽  
Gpu Computing ◽  
Graphics Processing Unit ◽  
General Purpose ◽  
Processing Unit ◽  
The Core ◽  
Critical Components ◽  
High Level

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.

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