scholarly journals Cannabis Personalization: Curating Personalized Cannabis Experiences Through Machine Learning

2021 ◽  
Author(s):  
JuliaB Ho
Keyword(s):  
Machine Learning ◽  
Specific Area ◽  
Health Practitioner ◽  
Arduous Task ◽  
User Data ◽  
Strain Type ◽  
Targeted Marketing ◽  
October 17 ◽  
Specific Symptoms ◽  
Medical Document

With the recent legalization of cannabis in Canada on October 17, 2018, the opportunity for emerging tech to complement and improve the cannabis experience is vast. The legalization of an industry that has been operating in the dark for decades means ample newfound opportunity for government and corporate-funded collaboration, development and research. A specific area of opportunity for growth within the cannabis sector is through personalization. Personalization is often performed via artificial intelligence—specifically machine learning—to develop a customized experience for users on various platforms. This is usually with the intention of targeted marketing. And while mass data collection serves the user by streamlining content to their assumed preferences, which then often directs them to businesses and products, product-tailoring still has vast potential for growth. Though a medical document for cannabis from a health practitioner may include broadband components to look out for, like “THC” and “CBD”, or even suggest ratios of those cannabinoids there is typically no specification on strain type and best consumption methods. Because the effects that cannabis has on a user varies from individual to individual and is dependent on not only their biometrics, but the various other terpenes and cannabinoids that exist in each strain beyond THC and CBD, cannabis users are missing out on opportunities to make the most of their use. Especially for those interested in cannabis to relieve specific symptoms, testing the vast amount of strains that exist and being able to identify the ideal product would be an arduous task on one’s own. Jibed is an app that would use the aggregation of user data to prescribe the most suitable strain of cannabis for that individual based on their specific conditions and body metrics. As the majority of the target audience (cannabis users in Canada) are already logged on to a multitude of data collecting apps (music, health, social, etc.), there is no shortage of data. The app would consider all the implications of the data, from one’s health to mood deduced from the music they're listening to -- just to name some -- in order to achieve optimal prescriptions.

scholarly journals Cannabis Personalization: Curating Personalized Cannabis Experiences Through Machine Learning

2021 ◽  
Author(s):  
JuliaB Ho
Keyword(s):  
Machine Learning ◽  
Specific Area ◽  
Health Practitioner ◽  
Arduous Task ◽  
User Data ◽  
Strain Type ◽  
Targeted Marketing ◽  
October 17 ◽  
Specific Symptoms ◽  
Medical Document

With the recent legalization of cannabis in Canada on October 17, 2018, the opportunity for emerging tech to complement and improve the cannabis experience is vast. The legalization of an industry that has been operating in the dark for decades means ample newfound opportunity for government and corporate-funded collaboration, development and research. A specific area of opportunity for growth within the cannabis sector is through personalization. Personalization is often performed via artificial intelligence—specifically machine learning—to develop a customized experience for users on various platforms. This is usually with the intention of targeted marketing. And while mass data collection serves the user by streamlining content to their assumed preferences, which then often directs them to businesses and products, product-tailoring still has vast potential for growth. Though a medical document for cannabis from a health practitioner may include broadband components to look out for, like “THC” and “CBD”, or even suggest ratios of those cannabinoids there is typically no specification on strain type and best consumption methods. Because the effects that cannabis has on a user varies from individual to individual and is dependent on not only their biometrics, but the various other terpenes and cannabinoids that exist in each strain beyond THC and CBD, cannabis users are missing out on opportunities to make the most of their use. Especially for those interested in cannabis to relieve specific symptoms, testing the vast amount of strains that exist and being able to identify the ideal product would be an arduous task on one’s own. Jibed is an app that would use the aggregation of user data to prescribe the most suitable strain of cannabis for that individual based on their specific conditions and body metrics. As the majority of the target audience (cannabis users in Canada) are already logged on to a multitude of data collecting apps (music, health, social, etc.), there is no shortage of data. The app would consider all the implications of the data, from one’s health to mood deduced from the music they're listening to -- just to name some -- in order to achieve optimal prescriptions.

scholarly journals A Survey on Twitter Sentimental Analysis with Machine Learning Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 462
Author(s):  
G Krishna Chaitanya ◽  
Dinesh Reddy Meka ◽  
Vakalapudi Surya Vamsi ◽  
M V S Ravi Karthik
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Buying Behavior ◽  
Online Retail ◽  
User Reviews ◽  
Business Plans ◽  
Learning Techniques ◽  
User Data

Sentiment or emotion behind a tweet from Twitter or a post from Facebook can help us answer what opinions or feedback a person has. With the advent of growing user-generated blogs, posts and reviews across various social media and online retails, calls for an understanding of these afore mentioned user data acts as a catalyst in building Recommender systems and drive business plans. User reviews on online retail stores influence buying behavior of customers and thus complements the ever-growing need of sentiment analysis. Machine Learning helps us to read between the lines of tweets by proving us with various algorithms like Naïve Bayes, SVM, etc. Sentiment Analysis uses Machine Learning and Natural Language Processing (NLP) to extract, classify and analyze tweets for sentiments (emotions). There are various packages and frameworks in R and Python that aid in Sentiment Analysis or Text Mining in general. 

scholarly journals Construction of the Luxury Marketing Model Based on Machine Learning Classification Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qiaoshan Chen ◽  
Shousong Cai ◽  
Xiaomin Gu
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Consumer Market ◽  
Luxury Goods ◽  
Machine Learning Classification ◽  
Marketing Model ◽  
Strategy Model ◽  
Sales And Marketing ◽  
Targeted Marketing ◽  
Marketing Methods

China has become the world’s largest luxury goods consumer market due to its population base. In view of the bright prospects of the luxury consumer market, major companies have entered and want to get a share. For the luxury goods industry, traditional mass marketing methods are not able to serve corporate sales and marketing strategies more effectively, and targeted marketing is clearly much more efficient than randomized marketing. Therefore, in this paper, based on consumer buying habits and characteristics data of luxury goods, the paper uses a machine learning algorithm to build a personalized marketing strategy model. And the paper uses historical data to model and form deductions to predict the purchase demand of each consumer and evaluate the possibility of customers buying different goods, including cosmetics, jewelry, and clothing.

scholarly journals Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms, and Evaluation

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1932
Author(s):  
Ramyar Saeedi ◽  
Keyvan Sasani ◽  
Assefaw H. Gebremedhin
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Mobile Health ◽  
Health Monitoring ◽  
Learning Models ◽  
Healthcare Applications ◽  
User Data ◽  
Adaptation Cost ◽  
Mobile Health Monitoring ◽  
Machine Learning Models

Mobile health monitoring plays a central role in the future of cyber physical systems (CPS) for healthcare applications. Such monitoring systems need to process user data accurately. Unlike in other human-centered CPS, in healthcare CPS, the user functions in multiple roles all at the same time: as an operator, an actuator, the physical environment and, most importantly, the target that needs to be monitored in the process. Therefore, mobile health CPS devices face highly dynamic settings generally, and accuracy of the machine learning models the devices employ may drop dramatically every time a change in setting happens. Novel learning architecture that specifically address challenges associated with dynamic environments are therefore needed. Using active learning and transfer learning as organizing principles, we propose a collaborative multiple-expert architecture and accompanying algorithms for the design of machine learning models that autonomously adapt to a new configuration, context, or user need. Specifically, our architecture and its constituent algorithms are designed to manage heterogeneous knowledge sources or experts with varying levels of confidence and type while minimizing adaptation cost. Additionally, our framework incorporates a mechanism for collaboration among experts to enrich their knowledge, which in turn decreases both cost and uncertainty of data labeling in future steps. We evaluate the efficacy of the architecture using two publicly available human activity datasets. We attain activity recognition accuracy of over 85 % (for the first dataset) and 92 % (for the second dataset) by labeling only 15 % of unlabeled data.

scholarly journals FPETD: Fault-Tolerant and Privacy-Preserving Electricity Theft Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Siliang Dong ◽  
Zhixin Zeng ◽  
Yining Liu
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Fault Tolerant ◽  
Privacy Preserving ◽  
User Privacy ◽  
Accuracy Rate ◽  
Electricity Theft ◽  
User Data ◽  
Dimensionality Reduction Method ◽  
Better Than

Electricity theft occurs from time to time in the smart grid, which can cause great losses to the power supplier, so it is necessary to prevent the occurrence of electricity theft. Using machine learning as an electricity theft detection tool can quickly lock participants suspected of electricity theft; however, directly publishing user data to the detector for machine learning-based detection may expose user privacy. In this paper, we propose a real-time fault-tolerant and privacy-preserving electricity theft detection (FPETD) scheme that combines n -source anonymity and a convolutional neural network (CNN). In our scheme, we designed a fault-tolerant raw data collection protocol to collect electricity data and cut off the correspondence between users and their data, thereby ensuring the fault tolerance and data privacy during the electricity theft detection process. Experiments have proven that our dimensionality reduction method makes our model have an accuracy rate of 92.86% for detecting electricity theft, which is much better than others.

scholarly journals Grain Yield Estimation in Cultivated Land Using Machine Learning Techniques

2021 ◽  
Vol 36 (2) ◽  
pp. 70-75
Author(s):  
Dr.K. Venkata Nagendra ◽  
Dr.B. Prasad ◽  
K.T.P.S. Kumar ◽  
K.S. Raghuram ◽  
Dr.K. Somasundaram
Keyword(s):  
Machine Learning ◽  
Market Price ◽  
Specific Area ◽  
Continuous Variable ◽  
Machine Learning Techniques ◽  
Accurate Information ◽  
Agricultural Crop ◽  
Agricultural Field ◽  
Learning Techniques ◽  
Selection Of

Agriculture contributes approximately 28 percent of India's GDP, and agriculture employs approximately 65 percent of the country's labor force. India is the world's second-largest agricultural crop producer. Agriculture is not only an important part of the expanding economy, but it is also necessary for our survival. The technological contribution could assist the farmer in increasing his yield. The selection of each crop is critical in the planning of agricultural production. The selection of crops will be influenced by a variety of factors, including market price, production rate, and the policies of the various government departments. Numerous changes are required in the agricultural field in order to improve the overall performance of our Indian economy. By using machine learning techniques that are easily applied to the farming sector we can improve agriculture. Along with all of the advancements in farming machinery and technology, the availability of useful and accurate information about a variety of topics plays an important role in the success of the industry. It is a difficult task to predict agricultural output since it depends on a number of variables, such as irrigation, ultraviolet (UV), insect killers, stimulants & the quantity of land enclosed in that specific area. It is proposed in this article that two distinct Machine Learning (ML) methods be used to evaluate the yields of the crops. The two algorithms, SVR and Linear Regression, have been well suited to validate the variable parameters of the continuous variable estimate with 185 acquired data points.

scholarly journals A Wide Database for Future Studies Aimed at Improving Early Recognition of Candidemia

2021 ◽  
Author(s):  
Sara Mora ◽  
Daniele Roberto Giacobbe ◽  
Chiara Russo ◽  
Elia Diana ◽  
Alessio Signori ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Recognition ◽  
Clinical Signs ◽  
Signs And Symptoms ◽  
Optimal Level ◽  
Risk Scores ◽  
High Morbidity ◽  
Rapid Tests ◽  
Diagnosis Accuracy ◽  
Specific Symptoms

Invasive candidiasis is associated with high morbidity and mortality in critically ill patients, i.e. patients admitted to Intensive Care Units (ICUs) or in surgical wards. There are no clinical signs or specific symptoms and even though early diagnosis risk scores and rapid tests are available, none of such strategies has an equally-optimal level of sensitivity and specificity. In the era of Electronic Health Records (EHRs), several clinical studies exploited Machine Learning (ML) models and large database of features to improve the diagnosis accuracy. The main aim of this work is to build a wide dataset which can be exploited to apply ML models to further improve the early recognition of candidemia at the bedside of patients with compatible signs and symptoms.

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