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Chapter and Conference Paper
Mining User Behavioral Rules from Smartphone Data Through Association Analysis
The increasing popularity of smart mobile phones and their powerful sensing capabilities have enabled the collection of rich contextual information and mobile phone usage records through the device logs. This ...
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Chapter and Conference Paper
An Improved Naive Bayes Classifier-Based Noise Detection Technique for Classifying User Phone Call Behavior
The presence of noisy instances in mobile phone data is a fundamental issue for classifying user phone call behavior (i.e., accept, reject, missed and outgoing), with many potential negative consequences. The ...
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Article
Research issues in mining user behavioral rules for context-aware intelligent mobile applications
Context awareness in smart mobile applications is a growing area of study because of its intelligence in the applications. To build context-aware intelligent applications, mining contextual behavioral rules of in...
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Article
Open AccessRecencyMiner: mining recency-based personalized behavior from contextual smartphone data
Due to the advanced features in recent smartphones and context-awareness in mobile technologies, users’ diverse behavioral activities with their phones and associated contexts are recorded through the device l...
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Article
Open AccessEffectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage
Due to the increasing popularity of recent advanced features and context-awareness in smart mobile phones, the contextual data relevant to users’ diverse activities with their phones are recorded through the d...
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Article
Open AccessContext-aware rule learning from smartphone data: survey, challenges and future directions
Smartphones are considered as one of the most essential and highly personal devices of individuals in our current world. Due to the popularity of context-aware technology and recent developments in smartphones...
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Chapter and Conference Paper
Cyber Intrusion Detection Using Machine Learning Classification Techniques
As the alarming growth of connectivity of computers and the significant number of computer-related applications increase in recent years, the challenge of fulfilling cyber-security is increasing consistently. ...
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Chapter and Conference Paper
Crime Prediction Using Spatio-Temporal Data
A crime is an action which constitutes a punishable offence by law. It is harmful for society so as to prevent the criminal activity, it is important to understand crime. Data driven researches are useful to prev...
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Chapter and Conference Paper
A Rule-Based Expert System to Assess Coronary Artery Disease Under Uncertainty
The coronary artery disease (CAD) occurs from the narrowing and damaging of major blood vessels or arteries. It has become the most life-threatening disease in the world, especially in the South Asian region. ...
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Chapter and Conference Paper
Rice Leaf Diseases Recognition Using Convolutional Neural Networks
The rice leaf suffers from several bacterial, viral, or fungal diseases and these diseases reduce rice production significantly. To sustain rice demand for a vast population globally, the recognition of rice l...
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Article
E-MIIM: an ensemble-learning-based context-aware mobile telephony model for intelligent interruption management
Nowadays, mobile telephony interruptions in our daily life activities are common because of the inappropriate ringing notifications of incoming phone calls in different contexts. Such interruptions may impact ...
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Article
BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model
This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones. In the area of machine learning and data science, a tree-like model as ...
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Article
Open AccessCybersecurity data science: an overview from machine learning perspective
In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from c...
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Article
Open AccessContext pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling
Nowadays, machine learning classification techniques have been successfully used while building data-driven intelligent predictive systems in various application areas including smartphone apps. For an effecti...
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Chapter
Recency-Based Updating and Dynamic Management of Contextual Rules
In the previous chapter, we have presented an approach for discovering behavioral rules of individual mobile phone users based on multi-dimensional contexts (temporal, spatial, and social context) utilizing th...
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Chapter
Deep Learning for Contextual Mobile Data Analytics
Deep learning is considered as a part of the broader family of machine learning methods, which is based on artificial neural networks with representation learning. In the earlier chapters, we have presented me...
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Chapter and Conference Paper
An Effective Heart Disease Prediction Model Based on Machine Learning Techniques
This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing appro...
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Chapter and Conference Paper
Predicting Individual Substance Abuse Vulnerability Using Machine Learning Techniques
Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol. Continuous use of these substances can ultimately lead a human to disastrous conseq...
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Chapter
Application Scenarios and Basic Structure for Context-Aware Machine Learning Framework
Context-aware machine learning typically focuses on applications that learn from contextual data and develop their decision-making abilities over time. To make intelligent decisions in different context-aware ...
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Chapter and Conference Paper
Text Classification Using Convolution Neural Networks with FastText Embedding
Text classification has a growing interest among NLP researchers due to its tremendous availability on online platforms and emergence on various Web 2.0 applications. Recently, text classification in resource-...
