scholarly journals A Mobile Platform for Food Donation and Delivery System using AI and Machine Learning

2021 ◽  
Author(s):  
George Zhou ◽  
Marisabel Chang ◽  
Yu Sun
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Orange County ◽  
Qualitative Evaluation ◽  
Free Food ◽  
Food Sources ◽  
Multiple Constraints ◽  
Software Application ◽  
Food Pantries ◽  
To Receive

Within the last year through the turmoil of the Covid-19 pandemic, an increasing number of families and individuals are experiencing food insecurity due to a loss of job, illnesses, or other financial struggles [4]. Many families in the Orange County area and abroad are turning to free food sources such as community food pantries or banks. Using specified surveys to food insecure families, we discovered a need for a solution to enhance the accessibility and usability of food pantries [5]. Therefore, we created a software application that uses artificial intelligence to locate specific items for users to request, and allow volunteers to see those requests and pick up the resources from food pantries, and deliver them directly to the homes of individuals. This paper shows the process in which this idea was created and how it was applied, along with the conduction of the qualitative evaluation of the approach. The results show that the software application allowed families and individuals to receive quality groceries at a much higher frequency, regardless of multiple constraints.

scholarly journals Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends and Directions

2019 ◽  
Author(s):  
Antonella Petrillo ◽  
Marta Travaglioni ◽  
Fabio De Felice ◽  
Raffaele Cioffi ◽  
Giuseppina Piscitelli
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industry 4.0 ◽  
Empirical Studies ◽  
Data Series ◽  
Before And After ◽  
Machine Learning Applications ◽  
History Of ◽  
The Subject ◽  
To Receive

The history of Artificial Intelligence (AI) development dates to the 40s. The researchers showed strong expectations until the 70s, when they began to encounter serious difficulties and investments were greatly, reduced. With the introduction of the Industry 4.0, one of the techniques adopted for AI implementation is Machine Learning (ML) that focuses on the machines ability to receive data series and learn on their own. Given the considerable importance of the subject, researchers have completed many studies on ML to ensure that machines are able to replace or relieve human tasks. This research aims to analyze, systematically, the literature on several aspects, including publication year, authors, scientific sector, country, institution, keywords. Analyzing existing literature on AI is a necessary stage to recommend policy on the matter. The analysis has been done using Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software have been used to complete them. Literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty-two articles were reviewed and classified. A first interesting result is the greater number of works published by USA and the increasing interest after the birth of Industry 4.0.

An Intelligent System to Automate Humidity Monitoring and Humidifier Control using Internet-of-Things (IoT) and Artificial Intelligence

2021 ◽  
Author(s):  
Qian Zhang ◽  
Yu Sun
Keyword(s):  
Artificial Intelligence ◽  
Control System ◽  
Side Effects ◽  
Intelligent System ◽  
Qualitative Evaluation ◽  
Manual Control ◽  
Air Conditioners ◽  
Software Application ◽  
The World ◽  
Humidity Monitoring

Air conditioners are widely used in family homes all over the world. However, the side effects of using air conditioners and dehumidification can cause health problems if people remain in lowhumidity environments. This paper traces the development of a software application and system to create an intelligent humidifier that automatically turns on or off for convenience or for those who cannot engage manual control. We applied our application to a humidifier for several days and conducted a qualitative evaluation of the approach. Results affirmed the usability and capacity of our automatic control system.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

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