91 research outputs found
Integration of blockchains with management information systems
In the era of the fourth industrial revolution (Industry 4.0), many Management Information Systems (MIS) integrate real-time data collection and use technologies such as big data, machine learning, and cloud computing, to foster a wide range of creative innovations, business improvements, and new business models and processes. However, the integration of blockchain with MIS offers the blockchain trilemma of security, decentralisation and scalability. MIS are usually Web 2.0 clientserver applications that include the front end web systems and back end databases; while blockchain systems are Web 3.0 decentralised applications. MIS are usually private systems that a single party controls and manages; while blockchain systems are usually public, and any party can join and participate. This paper clariļ¬es the key concepts and illustrates with ļ¬gures, the implementation of public, private and consortium blockchains on the Ethereum platform. Ultimately, the paper presents a framework for building a private blockchain system on the public Ethereum blockchain. Then,integrating the Web 2.0 client-server applications that are commonly used in MIS with Web 3.0 decentralised blockchain applications
The state-of-the-art in personalized recommender systems for social networking
With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match usersā personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0
Twitter analysis for depression on social networks based on sentiment and stress
Detecting words that express negativity in a social media message is one step towards detecting depressive moods. To understand if a Twitter user could exhibit depression over a period of time, we applied techniques in stages to discover words that are negative in expression. Existing methods either use a single step or a data subset, whereas we applied a multi-step approach which allowed us to identify potential users and then discover the words that expressed negativity by these users. We address some Twitter specific characteristics in our research. One of which is that Twitter data can be very large, hence our desire to be able to process the data efficiently. The other is that due to its enforced character limitation, the style of writing makes interpreting and obtaining the semantic meaning of the words more challenging. Results show that the sentiment of these words can be obtained and scored efficiently as the computation on these dataset were narrowed to only these selected users. We also obtained the stress scores which correlated well with negative sentiment expressed in the content. This work shows that by first identifying users and then using methods to discover words can be a very effective technique
Using Information Filtering in Web Data Mining Process
Web service-oriented Grid is becoming a standard for achieving loosely coupled distributed computing. Grid services could easily be specified with web-service based interfaces. In this paper we first envisage a realistic Grid market with players such as end-users, brokers and service providers participating co-operatively with an aim to meet requirements and earn profit. End-users wish to use functionality of Grid services by paying the minimum possible price or price confined within a specified budget, brokers aim to maximise profit whilst establishing a SLA (Service Level Agreement) and satisfying end-user needs and at the same time resisting the volatility of service execution time and availability. Service providers aim to develop price models based on end-user or broker demands that will maximise their profit. In this paper we focus on developing stochastic approaches to end-user workflow scheduling that provides QoS guarantees by establishing a SLA. We also develop a novel 2-stage stochastic programming technique that aims at establishing a SLA with end-users regarding satisfying their workflow QoS requirements. We develop a scheduling (workload allocation) technique based on linear programming that embeds the negotiated workflow QoS into the program and model Grid services as generalised queues. This technique is shown to outperform existing scheduling techniques that don't rely on real-time performance information
A case study of predicting banking customers behaviour by using data mining
Data Mining (DM) is a technique that examines information stored in large database or data warehouse and find the patterns or trends in the data that are not yet known or suspected. DM techniques have been applied to a variety of different domains including Customer Relationship Management CRM). In this research, a new Customer Knowledge Management (CKM) framework based on data mining is proposed. The proposed data mining framework in this study manages relationships between banking organizations and their customers. Two typical data mining techniques - Neural Network and Association Rules - are applied to predict the behavior of customers and to increase the decision-making processes for recalling valued customers in banking industries. The experiments on the real world dataset are conducted and the different metrics are used to evaluate the performances of the two data mining models. The results indicate that the Neural Network model achieves better accuracy but takes longer time to train the model
An ensemble-based decision tree approach for educational data mining
Nowadays, data mining and machine learning techniques are applied to a variety of different topics (e. g., healthcare and disease, security, decision support, sentiment analysis, education, etc.). Educational data mining investigates the performance of students and gives solutions to enhance the quality of education. The aim of this study is to use different data mining and machine learning algorithms on actual data sets related to students. To this end, we apply two decision tree methods. The methods can create several simple and understandable rules . Moreover, the performance of a decision tree is optimized by using an ensemble technique named Rotation Forest algorithm. Our findings indicate that the Rotation Forest algorithm can enhance the performance of decision trees in terms of different metrics. In addition, we found that the size of tree generated by decision trees ensemble were bigger than simple ones. This means that the proposed methodology can reveal more information concerning simple rules
Associations between exposure to and expression of negative opinions about Human Papillomavirus vaccines on social media: an observational study
Background
Groups and individuals that seek to negatively influence public opinion about the safety and value of vaccination are active in online and social media and may influence decision making within some communities.
Objective
We sought to measure whether exposure to negative opinions about human papillomavirus (HPV) vaccines in Twitter communities is associated with the subsequent expression of negative opinions by explicitly measuring potential information exposure over the social structure of Twitter communities.
Methods
We hypothesized that prior exposure to opinions rejecting the safety or value of HPV vaccines would be associated with an increased risk of posting similar opinions and tested this hypothesis by analyzing temporal sequences of messages posted on Twitter (tweets). The study design was a retrospective analysis of tweets related to HPV vaccines and the social connections between users. Between October 2013 and April 2014, we collected 83,551 English-language tweets that included terms related to HPV vaccines and the 957,865 social connections among 30,621 users posting or reposting the tweets. Tweets were classified as expressing negative or neutral/positive opinions using a machine learning classifier previously trained on a manually labeled sample.
Results
During the 6-month period, 25.13% (20,994/83,551) of tweets were classified as negative; among the 30,621 users that tweeted about HPV vaccines, 9046 (29.54%) were exposed to a majority of negative tweets. The likelihood of a user posting a negative tweet after exposure to a majority of negative opinions was 37.78% (2780/7361) compared to 10.92% (1234/11,296) for users who were exposed to a majority of positive and neutral tweets corresponding to a relative risk of 3.46 (95% CI 3.25-3.67, P<.001).
Conclusions
The heterogeneous community structure on Twitter appears to skew the information to which users are exposed in relation to HPV vaccines. We found that among users that tweeted about HPV vaccines, those who were more often exposed to negative opinions were more likely to subsequently post negative opinions. Although this research may be useful for identifying individuals and groups currently at risk of disproportionate exposure to misinformation about HPV vaccines, there is a clear need for studies capable of determining the factors that affect the formation and adoption of beliefs about public health interventions
Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion
Federated Learning (FL) is currently one of the most popular technologies in
the field of Artificial Intelligence (AI) due to its collaborative learning and
ability to preserve client privacy. However, it faces challenges such as
non-identically and non-independently distributed (non-IID) and data with
imbalanced labels among local clients. To address these limitations, the
research community has explored various approaches such as using local model
parameters, federated generative adversarial learning, and federated
representation learning. In our study, we propose a novel Clustered FedStack
framework based on the previously published Stacked Federated Learning
(FedStack) framework. The local clients send their model predictions and output
layer weights to a server, which then builds a robust global model. This global
model clusters the local clients based on their output layer weights using a
clustering mechanism. We adopt three clustering mechanisms, namely K-Means,
Agglomerative, and Gaussian Mixture Models, into the framework and evaluate
their performance. We use Bayesian Information Criterion (BIC) with the maximum
likelihood function to determine the number of clusters. The Clustered FedStack
models outperform baseline models with clustering mechanisms. To estimate the
convergence of our proposed framework, we use Cyclical learning rates.Comment: This work has been submitted to the ELSEVIER for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessibl
PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring
Reinforcement learning has been increasingly applied in monitoring
applications because of its ability to learn from previous experiences and can
make adaptive decisions. However, existing machine learning-based health
monitoring applications are mostly supervised learning algorithms, trained on
labels and they cannot make adaptive decisions in an uncertain complex
environment. This study proposes a novel and generic system, predictive deep
reinforcement learning (PDRL) with multiple RL agents in a time series
forecasting environment. The proposed generic framework accommodates virtual
Deep Q Network (DQN) agents to monitor predicted future states of a complex
environment with a well-defined reward policy so that the agent learns existing
knowledge while maximizing their rewards. In the evaluation process of the
proposed framework, three DRL agents were deployed to monitor a subject's
future heart rate, respiration, and temperature predicted using a BiLSTM model.
With each iteration, the three agents were able to learn the associated
patterns and their cumulative rewards gradually increased. It outperformed the
baseline models for all three monitoring agents. The proposed PDRL framework is
able to achieve state-of-the-art performance in the time series forecasting
process. The proposed DRL agents and deep learning model in the PDRL framework
are customized to implement the transfer learning in other forecasting
applications like traffic and weather and monitor their states. The PDRL
framework is able to learn the future states of the traffic and weather
forecasting and the cumulative rewards are gradually increasing over each
episode.Comment: This work has been submitted to the Springer for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessibl
Software development for managing nutrition intake for Type II Diabetes Mellitus
In this paper, we present the development and use
of a nutrition assessment software namely Nutritracs for Type II Diabetes Mellitus (T2DM). We conducted a case study in India to understand how diet counselling impacts upon Type II Diabetes Mellitus (T2DM) management. We also highlight practical challenges in conducting such studies in developing countries and how we have addressed them, and the promising outcome in T2DM management - reduced the dependency on insulin as a management tool
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