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The Internet of Things empowers citizens to interconnect their devices, such as smart phones, into large-scale participatory decentralized networks, which they can use to make real-time collective measurements as public good, for instance, crowd-sourcing the monitoring of traffic in a city. This approach is an alternative to big data analytics systems that are often expensive to access, privacy-intrusive...
Nowadays, with the development of navigation and location, many kinds of outdoor locating methods are surveyed and used, such as Global Position System (GPS) and LTE fingerprint-based positioning system. However, as the most compelling focus, the localization accuracies of these systems are limited by the location systems themselves. Therefore, in order to improve the localization accuracy, we should...
Botnet is a malicious software that can perform malicious activities, such as (Distributed Denial of Services) DDoS, spamming, phishing, key logging, click fraud, steal personal information and important data, etc. Botnets can replicate themselves without user consent. Several systems of botnet detection have been done by using a machine learning method with feature selection approach. Currently,...
The feasibility of large-scale decentralized networks for local computations, as an alternative to big data systems that are often privacy-intrusive, expensive and serve exclusively corporate interests, is usually questioned by network dynamics such as node leaves, failures and rejoins in the network. This is especially the case when decentralized computations performed in a network, such as the estimation...
This paper presents a novel framework for privacy aware collaborative information sharing for data classification. Data holders participating in this information sharing system, for global benefits are interested to model a classifier on whole dataset, but are ready to share their own table of data if a certain amount of privacy is guaranteed. To address this issue, we propose a privacy mechanism...
The demand for indoor localization services has led to the development of techniques that create a Fingerprint Map (FM) of sensor signals (e.g., magnetic, Wi-Fi, bluetooth) at designated positions in an indoor space and then use FM as a reference for subsequent localization tasks. With such an approach, it is crucial to assess the quality of the FM before deployment, in a manner disregarding data...
In speech recognition system, an improved multi-base neural network speech recognition model is proposed to solve the problem of long learning time and slow convergence rate of deep neural network. However, the improved model introduces a large number of parameters in the training process to make the model over-fitted in the test set, resulting in the deterioration of generalization ability and the...
To solve the problem that the traditional Bayesian filtering method has high dependency on system equation, GP-UKF and GP-CKF algorithms are used in this paper. The GP model is established by the off-line data generated by the system operation, and the model is used to replace the system equation in the traditional Bayesian filtering method. The simulation result shows that the two methods can detect...
On an average 9 out of 10 startups fail(industry standard). Several reasons are responsible for the failure of a startup including bad management, lack of funds, etc. This work aims to create a predictive model for startups based on many key things involved at various stages in the life of a startup. It is highly desirable to increase the success rate of startups and not much work have been done to...
Researchers in higher education are beginning to explore the potential of data mining in analyzing data for the purpose of giving quality service and needs of their graduates. Thus, educational data mining emerges as one tools to study academic data to identify patterns and help for decision making affecting the education. This paper predicts the employability of IT graduates using nine variables...
Energy consumption in Cloud computing is a significantissue and affects aspects such as the cost of energy, coolingin the data center and the environmental impact of cloud data centers. Monitoring and prediction provides the groundwork for improving the energy efficiency of data centers. This monitoring however is required to be fast and efficient without unnecessary overhead. It is also required...
There exists a base classification system for classification of problem tickets in the Enterprise domain. Different deep learning algorithms (Gated Recursive Unit and Long Short Term Memory) were investigated for solving the classification problem. Experiments were conducted for different parameters and layers for these algorithms. Paper brings out the architectures tried, results obtained, our conclusions...
In Data Mining classification plays prominent role in predicting outcomes. One of the best supervised classification techniques in Data Mining is Naive Bayes Classification. Naive Bayes Classification is good at predicting outcomes and often outperforms other classification techniques. One of the reasons behind the strong performance of Naive Bayes Classification is due to the assumption of conditional...
Computers and Smartphone's becomes vital part of everyday life and hence use of internet becomes more and more. Due to internet, computers are becomes vulnerable of different kinds of security threats. Therefore it is required that we need to have efficient security method in order to avoid leakage of important data or misuse of data. This security method is called as Intrusion Detection System (IDS)...
Reliability is a software quality characteristic that refer to the probability a system will work correctly over a period of time. Reliability prediction is important as it can be used to plan deployment, maintenance and test activities. This study assesses the efficiency of several techniques in software reliability model (SRM) selection and aims to find out the possible enhancement to improve software...
Multi-sensor based classification of professionals' activities plays a key role in ensuring the success of an his/her goals. In this paper we present the winning solution to the AAIA'15 Tagging Firefighter Activities at a Fire Scene data mining competition. The approach is based on a Random Forest classifier trained on an input data set with almost 5000 features describing the underlying time series...
An idea of contextual classifier ensembles extends the application possibility of additional measures of quality of base and ensemble classifiers in the process of contextual ensembles design. These measures besides the obvious classifier accuracy and diversity/similarity take under consideration the complexity, interpretability and significance. The complexity (the number of used measures and multi...
In this paper, we report on experiments on deployment of an extended distance-aware KinFu algorithm, designed to generate 3D model from Kinect data, onto depth frames extracted from stereo camera data. The proposed idea allows to suppress the Kinect usage limitation for outdoor sensing due to the IR interference with sunlight. Besides this, exploiting the stereo data enables a hybrid 3D reconstruction...
E-learning has become an essential factor in the modern educational system. In today's diverse student population, E-learning must recognize the differences in student personalities to make the learning process more personalized. The objective of this study is to create a data model to identify both the student personality type and the dominant preference based on the Myers-Briggs Type Indicator (MBTI)...
Process nonlinearity and time-varying behavior of industrial systems are the main factors for poor performance of online soft sensors. To ensure high predictive accuracy, adaptive soft sensor is a common practice. In this paper, an adaptive soft sensor based on moving window Gaussian process regression (GPR) is presented. To make the moving window strategy more efficient, a just-in-time learning (JITL)...
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