scholarly journals Advancements in Food Technology Using Artificial Intelligence- Deep Learning

2021 ◽  
pp. 1-9
Author(s):  
Viola A. Nwachukwu Nicholas-Okpara ◽  
Adaeze Joy Ubaka ◽  
Maryam Olanshile Adegboyega ◽  
Ifesinachi Anastacia Utazi ◽  
C. E. Chibudike ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Value Chain ◽  
Food Processing ◽  
Food Industry ◽  
New Technologies ◽  
Maximum Efficiency ◽  
Great Relevance ◽  
Increasing Demand ◽  
Efficiency And Productivity

The food industry has continued to evolve in terms of technologies employed in food processing. These advancements are because of increasing demand of food. Many industries are beginning to explore new technologies to enhance maximum efficiency and productivity across the food value chain. Artificial Intelligence (AI) is one of the emerging technologies that have found great relevance in the food sector. AI is simply the creation of smart machines capable of exhibiting human intelligence. This technology uses algorithms like machine learning and deep learning to mimic human behavior. AI has continued to find relevance in food processing and has proven to be an added advantage to the industry. In this article, we considered the relevance of AI to the food industry, its various applications in food processing, benefits, and setbacks to its adoption in the food industry.

FinTech Adoption in China

2021 ◽  
pp. 166-173
Author(s):  
Gagan Kukreja
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Financial Services ◽  
New Technologies ◽  
Large Population ◽  
Financial Inclusion ◽  
High Penetration ◽  
Digital Payments ◽  
Almost All

Almost all financial services (especially digital payments) in China are affected by new innovations and technologies. New technologies such as blockchain, artificial intelligence, machine learning, deep learning, and data analytics have immensely influenced all most all aspects of financial services such as deposits, transactions, billings, remittances, credits (B2B and P2P), underwriting, insurance, and so on. Fintech companies are enabling larger financial inclusion, changing in lifestyle and expenditure behavior, better and fast financial services, and lots more. This chapter covers the development, opportunities, and challenges of financial sectors because of new technologies in China. This chapter throws the light on opportunities that emerged because of the large population of 1.4 billion people, high penetration, and access to the latest and affordable technology, affordable cost of smartphones, and government policies and regulations. Lastly, this chapter portrays the untapped potentials of Fintech in China.

scholarly journals Technologies used for food preservation using microwaves

2021 ◽  
Vol 286 ◽  
pp. 04008
Author(s):  
Elena Sorică ◽  
Cristian Marian Sorică ◽  
Mario Cristea ◽  
Iulia Andreea Grigore
Keyword(s):  
Food Processing ◽  
Food Industry ◽  
Nutritional Value ◽  
New Technologies ◽  
Rapid Heating ◽  
High Energy ◽  
Food Preservation ◽  
Processed Foods ◽  
Acting Mechanism ◽  
High Energy Efficiency

Food preservation is the process of treating food, with the aim of preserving its qualities for as long as possible. Extending the freshness period for processed foods has been and is a continuing challenge for producers in the food industry. New technologies and conservation methodologies are continuously researched, which will have as little effect as possible on the nutritional value of the products. Microwave food processing is constantly evolving, rapid heating and high energy efficiency are the major advantages of using this technology. The paper presents a study regarding the preservation of food products using microwaves, its acting mechanism and other applications of microwaves for food processing, as well as some installations and equipment that use this technology.

Artificial Intelligence in Medicine

2021 ◽  
pp. 49-68
Author(s):  
Gia Merlo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
New Technologies ◽  
New Technology ◽  
Technological System ◽  
Medical Professionals ◽  
Human Intelligence ◽  
Connected Health ◽  
The Future

Disruptive forces are challenging the future of medicine. One of the key forces bringing change is the development of artificial intelligence (AI). AI is a technological system designed to perform tasks that are commonly associated with human intelligence and ability. Machine learning is a subset of AI, and deep learning is an aspect of machine learning. AI can be categorized as either applied or generalized. Machine learning is key to applied AI; it is dynamic and can become more accurate through processing different results. Other new technologies include blockchain, which allows for the storage of all of patients’ records to create a connected health ecosystem. Medical professionals ought to be willing to accept new technology, while also developing the skills that technology will not be able to replicate.

scholarly journals Earth Observation and Artificial Intelligence for Improving Safety to Navigation in Canada Low-Impact Shipping Corridors

2020 ◽  
Vol 9 (6) ◽  
pp. 383
Author(s):  
René Chénier ◽  
Mesha Sagram ◽  
Khalid Omari ◽  
Adam Jirovec
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Satellite Imagery ◽  
New Technologies ◽  
Earth Observation ◽  
Machine Learning Techniques ◽  
The Arctic ◽  
Marine Transportation ◽  
Processing Times ◽  
The Government

In 2014, through the World-Class Tanker Safety System (WCTSS) initiative, the Government of Canada launched the Northern Marine Transportation Corridors (NMTC) concept. The corridors were created as a strategic framework to guide Federal investments in marine transportation in the Arctic. With new government investment, under the Oceans Protection Plan (OPP), the corridors initiative, known as the Northern Low-Impact Shipping Corridors, will continue to be developed. Since 2016, the Canadian Hydrographic Service (CHS) has been using the corridors as a key layer in a geographic information system (GIS) model known as the CHS Priority Planning Tool (CPPT). The CPPT helps CHS prioritize its survey and charting efforts in Canada’s key traffic areas. Even with these latest efforts, important gaps in the surveys still need to be filled in order to cover the Canadian waterways. To help further develop the safety to navigation and improve survey mission planning, CHS has also been exploring new technologies within remote sensing. Under the Government Related Initiatives Program (GRIP) of the Canadian Space Agency (CSA), CHS has been investigating the potential use of Earth observation (EO) data to identify potential hazards to navigation that are not currently charted on CHS products. Through visual interpretation of satellite imagery, and automatic detection using artificial intelligence (AI), CHS identified several potential hazards to navigation that had previously gone uncharted. As a result, five notices to mariners (NTMs) were issued and the corresponding updates were applied to the charts. In this study, two AI approaches are explored using deep learning and machine learning techniques: the convolution neural network (CNN) and random forest (RF) classification. The study investigates the effectiveness of the two models in identifying shoals in Sentinel-2 and WorldView-2 satellite imagery. The results show that both CNN and RF models can detect shoals with accuracies ranging between 79 and 94% over two study sites; however, WorldView-2 images deliver results with higher accuracy and lower omission errors. The high processing times of using high-resolution imagery and training a deep learning model may not be necessary in order to quickly scan images for shoals; but training a CNN model with a large training set may lead to faster processing times without the need to train individual images.

scholarly journals A Survey on Explainability: Why Should We Believe the Accuracy of A Model?

2020 ◽  
Author(s):  
Prarthana Dutta ◽  
Naresh Babu Muppalaneni ◽  
Ripon Patgiri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
New Technologies ◽  
Research Community ◽  
Learning Technologies ◽  
The World ◽  
Current Scenario ◽  
Day By Day ◽  
Modern Technologies

The world has been evolving with new technologies and advances day-by-day. With the advent of various learning technologies in every field, the research community is able to provide solution in every aspect of life with the applications of Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, etc. However, with such high achievements, it is found to lag behind the ability to provide explanation against its prediction. The current situation is such that these modern technologies are able to predict and decide upon various cases more accurately and speedily than a human, but failed to provide an answer when the question of why to trust its prediction is put forward. In order to attain a deeper understanding into this rising trend, we explore a very recent and talked-about novel contribution which provides rich insight on a prediction being made -- ``Explainability.'' The main premise of this survey is to provide an overview for researches explored in the domain and obtain an idea of the current scenario along with the advancements published to-date in this field. This survey is intended to provide a comprehensive background of the broad spectrum of Explainability.

scholarly journals Application of artificial intelligence (AI) in food industry

2020 ◽  
Vol 13 (1) ◽  
pp. 171-178
Author(s):  
Chidinma-Mary-Agbai
Keyword(s):  
Artificial Intelligence ◽  
New Product Development ◽  
Food Industry ◽  
Consumer Preference ◽  
New Product ◽  
Compliance Monitoring ◽  
Cultural Changes ◽  
Safety Compliance ◽  
Skill Requirements ◽  
Increasing Demand

Artificial intelligence (AI) is the theory and development of computer systems able to perform tasks normally requiring human intelligence. With teeming competition and increasing demand in the food industry, the industry has begun to embrace AI technologies in a bid to maximize profits and explore new ways to reach and serve the consumers. AI has been successfully deployed for applications such as sorting fresh produce, managing supply chain, food safety compliance monitoring, effective cleaning in place systems, anticipating consumer preference and new product development with greater efficiency and savings on time and resources. However, there are challenges to adoption of AI technologies which include cost, cultural changes, expert skill requirements, transparency issues and one track minds. Despite these challenges, researches are on-going on optimized production process using AI but it is important to note that the benefits of AI application in food industry greatly outweigh its challenges.

scholarly journals AN EFFICIENT FOG ENABLED HEALTHCARE MAINTENANCE SYSTEM USING INTERNET OF THINGS WITH DEEP LEARNING STRATEGIES

2021 ◽  
Vol 9 (1) ◽  
pp. 659-665
Author(s):  
G. S. Gunanidhi, R. Krishnaveni
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Internet Of Things ◽  
Learning Strategies ◽  
New Technologies ◽  
Learning Strategy ◽  
Fog Computing ◽  
Health Records ◽  
Maintenance System ◽  
High Efficient

Internet of Things (IoT) is the ruling term now-a-days, in which it attracts several smart gadgets and application due to its robust nature and support. In healthcare industry several new technologies are required to improve the stability and provide transparent services to clients. The integration of healthcare maintenance system with respect to Internet of Things support leads to a drastic change in healthcare field as well as this provision provides huge advantages to users. This paper is intended to provide an intense healthcare maintenance scheme by using latest technologies such as Deep Learning, Internet of Things, Fog Computing and Artificial Intelligence. All these innovations are associated together to build a new deep learning strategy called Intense Health Analyzing Scheme (IHAS), in which this proposed approach provides all provisions to clients such as Doctors and Patients with respect to monitor the patient details from anywhere at anytime without any range boundaries. The Fog Computing is an innovative domain, in which it provides ability to the server to operate based on hurdle free processing logic. Artificial Intelligence logic is used to manipulate the health data based on previously trained health records, so that the predictions are more fine compare to the classical healthcare schemes. In traditional schemes it is difficult to raise an alert based on the emergency situation predictions, but in the proposed deep learning strategy assists the proposed approach to send an alert instantly if any emergency cases occurred on patient end. Generally the Fog Servers are used to reduce the occupancy of the storage server and provide reliable storage abilities to server, but in this proposed approach, the fog server is utilized for priority wise data handling nature and stores the health records accordingly. In this nature, the fog servers are handled and provide high efficient results to the clients in an innovative way. With the help of deep learning procedures, the health records are clearly prioritized and maintained into the server end for monitoring. For all this paper introduced a new logic of healthcare maintenance scheme IHAS to provide efficient support to patients as well as doctors in clear manner.

scholarly journals Review: Application of Artificial Intelligence in Phenomics

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4363
Author(s):  
Shona Nabwire ◽  
Hyun-Kwon Suh ◽  
Moon S. Kim ◽  
Insuck Baek ◽  
Byoung-Kwan Cho
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Data Collection ◽  
New Technologies ◽  
Plant Traits ◽  
Imaging Techniques ◽  
Plant Phenotyping ◽  
Plant Phenomics ◽  
Field Phenotyping ◽  
High Throughput Phenotyping

Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

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