http://localhost:8080/xmlui/handle/123456789/302 Tue, 23 Jun 2026 06:10:51 GMT 2026-06-23T06:10:51Z http://localhost:8080/xmlui/handle/123456789/3351 Title: DEVICE DISCOVERY AND CLASSIFICATION FOR INTERNET OF THINGS Authors: Priyadarshini, I; Taware, A.V. Abstract: : Internet of Things allows everything around the world to connect to Internet .These include devices such as thermostats, utility meters, headset with blue tooth enabled, irrigation pumps, sensors or control circuits for electric car engine. It connects things embedded with sensors like devices, appliances and machines to the Internet and allows these things to communicate and exchange data. The number of connected device on IOT will be huge .By the year 2020 it is predicted that the number of devices connected to the Internet will be around 50 billion. There exist a vast diversity of the things being connected to Internet .Each device has its unique rules and standards for communication. It would become difficult for any application to integrate a new device as well as in case of any security threat in network it would be difficult to identify the device which causes the suspicious traffic, In order to solve the above problem there exist a need to automatically discover, detect and perform classification of device that are communicating inside the network. Keywords Sun, 15 Jul 2018 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3351 2018-07-15T00:00:00Z http://localhost:8080/xmlui/handle/123456789/2968 Title: Evaluation of Multi-label Classifiers in Various Domains Using Decision Tree Authors: Tidake, V. S.; Sane, S. S. Abstract: One of the commonly used tasks in mining is classification, which can be performed using supervised learning approach. Because of digitization, lot of documents are available which need proper organization, termed as text categorization. But sometimes documents may reflect multiple semantic meanings, which represents multi-label learning. It is the method of associating a set of predefined classes to an unseen object depending on its properties. Different methods to do multi-label classification are divided into two groups, namely data transformation and algorithm adaptation. This paper focuses on the evaluation of eight algorithms of multi-label learning based on nine performance metrics using eight multi-label datasets, and evaluation is performed based on the results of experimentation. For all the multi-label classifiers used for experimentation, decision tree is used as a base classifier whenever required. Performance of different classifiers varies according to the size, label cardinality, and domain of the dataset. Fri, 15 Jun 2018 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/2968 2018-06-15T00:00:00Z http://localhost:8080/xmlui/handle/123456789/2739 Title: LARGE SCALE PERSON RE-IDENTIFICATION USING PART BASED DEEP HASHING Authors: Jain, P.; Jain, E.; Dadhich, S.; Tonpe, S.; Kolapkar, A. V. Abstract: It is important to perform real time search while Large scale Person re-identification on a large gallery. The conventional methods use to focus on discriminative learning which is probabilistic approach of learning. In this proposed project we attempt to use deep learning while integrating it with hashing which provides a framework to evaluate productivity, precision and reliability of Large scale Person re-identification. We use augmentation for creating artificial training images through different ways of processing or combination of multiple processing such as random rotation, shifts, shear and flips etc. We propose Part based deep Hashing (PDH) in which augmented images are the input of deep learning architecture. All the augmented images have different identity. We are using whole image and use it for training deep hashing architecture. We use a ternion loss function which calculates the hamming distance of the pedestrian image. The hamming distance of the images with same identity is smaller than the one with different identity. In this project we use standard large scale dataset specifically Market-1501 & Market-1501 +500K. Thu, 01 Mar 2018 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/2739 2018-03-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/2737 Title: DEVICE DISCOVERY AND CLASSIFICATION FOR INTERNET OF THINGS Authors: Priyadarshini; Taware, A .V. Abstract: Internet of Things allows everything around the world to connect to Internet .These include devices such as thermostats, utility meters, headset with blue tooth enabled, irrigation pumps, sensors or control circuits for electric car engine. It connects things embedded with sensors like devices, appliances and machines to the Internet and allows these things to communicate and exchange data. The number of connected device on IOT will be huge .By the year 2020 it is predicted that the number of devices connected to the Internet will be around 50 billion. There exist a vast diversity of the things being connected to Internet .Each device has its unique rules and standards for communication. It would become difficult for any application to integrate a new device as well as in case of any security threat in network it would be difficult to identify the device which causes the suspicious traffic, In order to solve the above problem there exist a need to automatically discover, detect and perform classification of device that are communicating inside the network. Thu, 01 Mar 2018 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/2737 2018-03-01T00:00:00Z