Indian Institute of Technology Bhubaneswar

About: Indian Institute of Technology Bhubaneswar is a education organization based out in Bhubaneswar, India. It is known for research contribution in the topics: Large Hadron Collider & Higgs boson. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

Deep Convolutional Generative Adversarial Network-Based Food Recognition Using Partially Labeled Data

Abstract: Traditional machine learning algorithms using hand-crafted feature extraction techniques (such as local binary pattern) have limited accuracy because of high variation in images of the same class (or intraclass variation) for food recognition tasks. In recent works, convolutional neural networks (CNNs) have been applied to this task with better results than all previously reported methods. However, they perform best when trained with large amount of annotated (labeled) food images. This is problematic when obtained in large volume, because they are expensive, laborious, and impractical. This article aims at developing an efficient deep CNN learning-based method for food recognition alleviating these limitations by using partially labeled training data on generative adversarial networks (GANs). We make new enhancements to the unsupervised training architecture introduced by Goodfellow et al. , which was originally aimed at generating new data by sampling a dataset. In this article, we make modifications to deep convolutional GANs to make them robust and efficient for classifying food images. Experimental results on benchmarking datasets show the superiority of our proposed method, as compared to the current state-of-the-art methodologies, even when trained with partially labeled training data.

A Novel Malware Analysis Framework for Malware Detection and Classification using Machine Learning Approach

Abstract: Nowadays, the digitization of the world is under a serious threat due to the emergence of various new and complex malware every day. Due to this, the traditional signature-based methods for detection of malware effectively become an obsolete method. The efficiency of the machine learning techniques in context to the detection of malwares has been proved by state-of-the-art research works. In this paper, we have proposed a framework to detect and classify different files (e.g., exe, pdf, php, etc.) as benign and malicious using two level classifier namely, Macro (for detection of malware) and Micro (for classification of malware files as a Trojan, Spyware, Ad-ware, etc.). Our solution uses Cuckoo Sandbox for generating static and dynamic analysis report by executing the sample files in the virtual environment. In addition, a novel feature extraction module has been developed which functions based on static, behavioral and network analysis using the reports generated by the Cuckoo Sandbox. Weka Framework is used to develop machine learning models by using training datasets. The experimental results using the proposed framework shows high detection rate and high classification rate using different machine learning algorithms

Performance Studies of 10 Metaheuristic Techniques in Determination of Damages for Large-Scale Spatial Trusses from Changes in Vibration Responses

Abstract: The determination of the location and severity of damage is a crucial task in the damage assessment of structures. In this study, 10 population-based metaheuristic techniques are applied to...

Decentralized Coded Caching for Shared Caches

Abstract: The demands of the clients in the client-server framework exhibit temporal variance leading to congestion in the network at random intervals. To alleviate this problem, popular data is loaded in cache memories scattered across the network. In the conventional cache framework, each user has an associated cache and cache loading is centrally coordinated. For large networks, a more practical approach is to make the loading of the caches decentralized. This letter considers the shared caching problem in which each cache can serve multiple clients. A new and optimal delivery scheme is proposed for the decentralized shared caching problem. The delivery scheme is shown to be optimal among all linear schemes, using techniques from index coding. It is shown that the rate achieved by the proposed scheme is comparable to the existing scheme which uses centralized prefetching.

Optimization of Four Wave Mixing Effect in Radio-over-Fiber for a 32-Channel 40-GBPS DWDM System

Abstract: In this paper, we have discussed in detail the four wave mixing (FWM) effect of dense wavelength division multiplexing (DWDM) in Radio-over-Fiber (RoF) system. A 32-channel 40-Gbps system is considered. FWM effect for various channel spacing, input power level, effective fiber area and modulation formats are analyzed. Different schemes for minimizing these effects are discussed for the first time. Considering all these effects, an optimized DWDM setup is designed using single parameter optimization (SPO) technique.