'Big Data' is the term that describes a large amount of datasets. Datasets like web logs, call records, medical records, military surveillance, photography archives, etc. are often so large and complex, and as...
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ISBN:
(纸本)9781509037056
'Big Data' is the term that describes a large amount of datasets. Datasets like web logs, call records, medical records, military surveillance, photography archives, etc. are often so large and complex, and as the data is stored in Big Data in the form of both structured and unstructured therefore, big data cannot be processed using database queries like SQL queries. In big data, malicious URLs have become a station for internet criminal activities such as drive- bydownload,information warfare, spamming and phishing. Malicious URLs detection techniques can be classified into Non-Machine Learning (e.g. blacklisting) and Machine learning approach (e.g. data mining techniques). Data mining helps in the analysis of large and complex datasets in order to detect common patterns or learn new *** data is the collection of large and complex datasets and the processing of these datasets can be done either by using tool like Hadoop or data mining algorithms. Data mining techniques can generate classification models which is used to manage data, modelling of data that helps to make prediction about whether it is malicious or legitimate. In this paper analysis of RIPPER i.e. JRip data mining algorithm has been done using WEKA tool. A training dataset of 6000 URLs has been made to train the JRip algorithm which is an implementation of RIPPER algorithm in WEKA. Training dataset will generate a model which is used to predict the testing dataset of 1050 URLs. Accuracy are calculated after testing process. Result shows JRip has an accuracy of 82%.
Incremental approaches may be used to speed up the learning process when a classification algorithm is dealing with big data bases. In this work we present a study on how the size and composition of the set of learnin...
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ISBN:
(纸本)9781509060351
Incremental approaches may be used to speed up the learning process when a classification algorithm is dealing with big data bases. In this work we present a study on how the size and composition of the set of learning examples that are given to an incremental algorithm affect its behaviour.
In computer vision, recognition of cursive alphabets is a difficult and challenging task. Cursive alphabet recognition has its own importance within the field of bank cheques, American postcard, medical prescription l...
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ISBN:
(纸本)9781509037056
In computer vision, recognition of cursive alphabets is a difficult and challenging task. Cursive alphabet recognition has its own importance within the field of bank cheques, American postcard, medical prescription letter, etc. The complexness of recognizing cursive alphabets lies within the stroke, inclination, size and the different handwriting styles. Here we have presented a survey on the recognition of cursive handwriting. Numerous methodologies are presented with their accuracy of recognition or recognition rate.
Naive users using a browser have no idea about the back-end of the page. The users might be tricked into giving away their credentials or downloading malicious data. Our aim is to create an extension for Chrome which ...
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ISBN:
(纸本)9781509037056
Naive users using a browser have no idea about the back-end of the page. The users might be tricked into giving away their credentials or downloading malicious data. Our aim is to create an extension for Chrome which will act as middleware between the users and the malicious websites, and mitigate the risk of users succumbing to such websites. Further, all harmful content cannot be exhaustively collected as even that is bound to continuous development. To counter this we are using machine learning- to train the tool and categorize the new content it sees every time into the particular categories so that corresponding action can be taken.
Brain-Computer interface (BCI) which aims at enabling users to perform tasks through their brain waves has been a feasible and worth developing solution for growing demand of healthcare. Current proposed BCI systems a...
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ISBN:
(纸本)9781509061839
Brain-Computer interface (BCI) which aims at enabling users to perform tasks through their brain waves has been a feasible and worth developing solution for growing demand of healthcare. Current proposed BCI systems are often with lower applicability and do not provide much help for reducing burdens of users because of the time-consuming preparation required by adopted wet sensors and the shortage of provided interactive functions. Here, by integrating a state visually evoked potential (SSVEP)-based BCI system and a robotic eating assistive system, we propose a non-invasive wireless steady state visually evoked potential (SSVEP)-based BCI eating assistive system that enables users with physical disabilities to have meals independently. The analysis compared different methods of classification and indicated the best method. The applicability of the integrated eating assistive system was tested by an Amyotrophic Lateral Sclerosis (ALS) patient, and a questionnaire reply and some suggestion are provided. Fifteen healthy subjects engaged the experiment, and an average accuracy of 91.35%, and information transfer rate (ITR) of 20.69 bit per min are achieved. For online performance evaluation, the ALS patient gave basic affirmation and provided suggestions for further improvement. In summary, we proposed a usable SSVEP-based BCI system enabling users to have meals independently. With additional adjustment of movement design of the robotic arm and classification algorithm, the system may offer users with physical disabilities a new way to take care of themselves.
In this paper, we describe practical application of trends in visualization of algorithms. Ourpositive results of the utilized and processed methodology of software development, confirmed byseveral research and survey...
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ISBN:
(纸本)9781605952840
In this paper, we describe practical application of trends in visualization of algorithms. Ourpositive results of the utilized and processed methodology of software development, confirmed byseveral research and surveys, we apply for our own software development. The software is primarilyintended to investigate the properties of Quick Sort- one of the most frequently used sorting algorithmin practice. It is also supplemented by visualization of the sorting process itself for the purpose ofinteractive, visual and motivating form of familiarization with the given algorithm.
Cognitive radio (CR) is a promising solution to improve the spectrum utilization. Spectrum handoff is an indispensable component in cognitive radio networks. There are two kinds of handoff scheme: proactive handoff an...
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ISBN:
(纸本)9788132225805;9788132225799
Cognitive radio (CR) is a promising solution to improve the spectrum utilization. Spectrum handoff is an indispensable component in cognitive radio networks. There are two kinds of handoff scheme: proactive handoff and reactive handoff. In reactive handoff, the predefined target spectrum list (PTSL) model can effectively improve the accuracy of handoff and reduce handoff latency. In this paper, we studied the sorting algorithm of PTSL model and put forward an improved algorithm. The traditional sorting algorithm just considered two elements, the channel bandwidth and quality. We add the predicted channel available time into consideration. When spectrum handoff occurs, the secondary user can choose the channel that not only the bandwidth and quality can satisfy the request, but also the available time can suit the business. Simulation results show that the improved algorithm can avoid the secondary handoff effectively and improve the performance of the whole system.
This paper presents two different control strategies to balance the capacitor voltage of sub-modules in modular multi-level converters. In the centralized control, sub-modules receive the switching state defined by a ...
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ISBN:
(纸本)9781509033881
This paper presents two different control strategies to balance the capacitor voltage of sub-modules in modular multi-level converters. In the centralized control, sub-modules receive the switching state defined by a proposed sorting algorithm in order to control the capacitor voltage. The proposed algorithm reduces the number of switching changes in comparison with the classical sorting algorithm used in literature. The local control based on a PI controller is integrated into the sub-modules, for this reason, all the cells in the arm receive the same modulation index, being modified according to voltage references. Both control strategies are evaluated and validated through simulation results.
Cardiovascular system is the most important part of human body which has role as distribution system of Oxygen and body's wastes. To do the job, there are more than 60.000 miles of blood vessels participated which...
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ISBN:
(纸本)9781509034772
Cardiovascular system is the most important part of human body which has role as distribution system of Oxygen and body's wastes. To do the job, there are more than 60.000 miles of blood vessels participated which can caused a problem if one of them are being clogged. Unfortunately, the conditions of clogged blood vessels or diseases caused by cardiovascular malfunction could not be detected in a plain view. In this matter, we proposed a design of wearable device which can detect the conditions. The device is equipped with a newly neural network algorithm, GLVQ-PSO, which can give recommendation of the heart status based on learned data. After the research is conducted, the algorithm produce better accuracy than LVQ, GLVQ and FNGLVQ in the high level language implementation. Yet, GLVQ-PSO still has relatively worse performance in its FPGA implementation.
Rapidly-Exploring Random Trees (RRT) is a sampling-based path planning method, which aims at finding the feasible path using a randomized data structure. But RRT usually can not find the optimal path. Although RRT* is...
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ISBN:
(纸本)9781509043644
Rapidly-Exploring Random Trees (RRT) is a sampling-based path planning method, which aims at finding the feasible path using a randomized data structure. But RRT usually can not find the optimal path. Although RRT* is an asymptotically optimal algorithm, its planning time scales poorly. In this paper, we propose an improved RRT algorithm incorporating obstacle boundary information to deal with these two problems. We classify obstacles in the environment into valuable and valueless obstacles. For the obstacle placed between the start and goal positions, it is regarded as valuable and we use its boundary information to make the path planner sample points around it to avoid other bad sampling points. Then the random tree will grow more intentionally to the goal position. By applying Partially Observed Markov Decision Process (POMDP) in our algorithm, we also can prove in theory that obstacle boundary information really improves RRT algorithm. In our experiments, we show that our algorithm can find a better feasible path faster than RRT.
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