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- 저자 : Prabhpreet (검색결과 5 건)
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Neuroimaging Techniques for Brain Computer Interface
Prabhpreet Kaur Bhatia,Anurag Sharma,Sanmati Kumar 보안공학연구지원센터 2015 International Journal of Bio-Science and Bio-Techn Vol.7 No.4
Brain-computer interface (BCI) is combination of hardware and software systems that allows the severely or partially disabled persons to communicate with their surroundings. The study of brain activities precisely is an important step in BCI system. Many invasive and non-invasive neuro-imaging techniques are being conducted. In this paper, a comparative analysis of these different approaches has been reviewed such as electro-encephalography (EEG), electro-corticograph (ECoG), magneto-encephalograph (MEG), intra-cortical neuron recording (INR), and magnetic resonance imaging (MRI).
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Epilepsy Seizure Detection Using Wavelet Support Vector Machine Classifier
Prabhpreet Kaur Bhatia,Anurag Sharma 보안공학연구지원센터 2016 International Journal of Bio-Science and Bio-Techn Vol.8 No.2
Epilepsy is a perilous neurological disease covering about 4-5% of total population of the world. Its main characteristics are seizures which occur due to certain disturbance in brain function. During epileptic seizures the patient is unaware of their physical as well as mental condition and hence physical injury may occur. Proper health care must be provided to the patients and this can be achieved only if the seizures are detected correctly in time. In this dissertation work, a system is designed using wavelet decomposition method and different training algorithms to train the neural network for classification of the EEG signals. The system was tested and compared with Support Vector Machine (SVM) classifier. The system accuracy comes out to be 99.97%.
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Surleen Kaur,Prabhpreet Kaur 한국멀티미디어학회 2019 The journal of multimedia information system Vol.6 No.1
Plants are very crucial for life on Earth. There is a wide variety of plant species available, and the number is increasing every year. Species knowledge is a necessity of various groups of society like foresters, farmers, environmentalists, educators for different work areas. This makes species identification an interdisciplinary interest. This, however, requires expert knowledge and becomes a tedious and challenging task for the non-experts who have very little or no knowledge of the typical botanical terms. However, the advancements in the fields of machine learning and computer vision can help make this task comparatively easier. There is still not a system so developed that can identify all the plant species, but some efforts have been made. In this study, we also have made such an attempt. Plant identification usually involves four steps, i.e. image acquisition, pre-processing, feature extraction, and classification. In this study, images from Swedish leaf dataset have been used, which contains 1,125 images of 15 different species. This is followed by pre-processing using Gaussian filtering mechanism and then texture and color features have been extracted. Finally, classification has been done using Multiclass-support vector machine, which achieved accuracy of nearly 93.26%, which we aim to enhance further.
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An Evaluation of Dynamic Java Bytecode Software Watermarking Algorithms
Krishan Kumar,Viney Kehar,Prabhpreet Kaur 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.7
In the era of Information technology, Software Piracy and security has become one of the most important issue in world. Numbers of techniques have been proposed and implemented to prevent software piracy and illegal modification. Among all the protection techniques, software watermarking technique which attempts to protect the software by embedding copyright notice or unique identifiers into software to prove the ownership of software. Software Watermarking discourage piracy; as a proof of purchase or authorship; also helps in tracking the source of illegal redistribution of copies of software. We evaluate the existing dynamic watermarking algorithms using them to watermark java bytecode files and then applying distortive attacks to each watermarked program by obfuscating. Our study has shown that some watermarks were removed as results of these transformations.
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Kaur, Surleen,Kaur, Prabhpreet Korea Multimedia Society 2019 The journal of multimedia information system Vol.6 No.2
Plants are very crucial for life on Earth. There is a wide variety of plant species available, and the number is increasing every year. Species knowledge is a necessity of various groups of society like foresters, farmers, environmentalists, educators for different work areas. This makes species identification an interdisciplinary interest. This, however, requires expert knowledge and becomes a tedious and challenging task for the non-experts who have very little or no knowledge of the typical botanical terms. However, the advancements in the fields of machine learning and computer vision can help make this task comparatively easier. There is still not a system so developed that can identify all the plant species, but some efforts have been made. In this study, we also have made such an attempt. Plant identification usually involves four steps, i.e. image acquisition, pre-processing, feature extraction, and classification. In this study, images from Swedish leaf dataset have been used, which contains 1,125 images of 15 different species. This is followed by pre-processing using Gaussian filtering mechanism and then texture and color features have been extracted. Finally, classification has been done using Multiclass-support vector machine, which achieved accuracy of nearly 93.26%, which we aim to enhance further.
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