Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Information risk analysis in a distributed mooc based software system using an optimized artificial neural network(Association for Computing Machinery acmhelp@acm.org, 2015) Sharath, N.; Parikh, S.S.; Chandrasekaran, K.Information security is of utmost importance to any organization. With the increasing number of attacks on private data, understanding the risk involved with handling and maintaining it is relevant. Although there are various methods to determine the risk associated with a certain organization's data, there is also a need to speed up the process of computation of this risk. This paper discusses the usage of Artificial Neural Networks that bodes well for the non linear nature of the threat vectors that affect risk involved in setting up a distributed MOOC based software system. An optimization to the existing methods is proposed that makes use of the bio inspired, Cuckoo Search Algorithm. With the concept of Levy Flights and Random Walks, this algorithm produces a much faster rate of convergence in calculation of the importance to be given to each threat vector in assessing the security of the software system. © 2015 ACM.Item Saliency prediction for visual regions of interest with applications in advertising(Springer Verlag service@springer.de, 2017) Jain, S.; Kamath S․, S.S.Human visual fixations play a vital role in a plethora of genres, ranging from advertising design to human-computer interaction. Considering saliency in images thus brings significant merits to Computer Vision tasks dealing with human perception. Several classification models have been developed to incorporate various feature levels and estimate free eye-gazes. However, for real-time applications (Here, real-time applications refer to those that are time, and often resource-constrained, requiring speedy results. It does not imply on-line data analysis), the deep convolution neural networks are either difficult to deploy, given current hardware limitations or the proposed classifiers cannot effectively combine image semantics with low-level attributes. In this paper, we propose a novel neural network approach to predict human fixations, specifically aimed at advertisements. Such analysis significantly impacts the brand value and assists in audience measurement. A dataset containing 400 print ads across 21 successful brands was used to successfully evaluate the effectiveness of advertisements and their associated fixations, based on the proposed saliency prediction model. © Springer International Publishing AG 2017.Item Zigbee-based wearable device for elderly health monitoring with fall detection(Springer Verlag, 2018) Yousuff, S.; Chaudary, S.K.; Meghana, N.P.; Ashwin, T.S.; Guddeti, G.Health monitoring devices have flooded the market. But there are very few that cater specifically to the needs of elderly people. Continuously monitoring some critical health parameters like heart rate, body temperature can be lifesaving when the elderly is not physically monitored by a caretaker. An important difference between a general health tracking device and one meant specifically for the elderly is the pressing need in the latter to be able to detect a fall. In case of an elderly person or a critical patient, an unexpected fall, if not attended to within a very short time span, can lead to disastrous consequences including death. We present a solution in the form of a wearable device which, along with monitoring the critical health parameters of the elderly person, can also detect an event of a fall and alert the caretaker. We make use of a 3-axis accelerometer embedded into the wearable to collect acceleration data from the movements of the elderly. We have presented two algorithms for fall detections—one based on a threshold and the other based on a neural network and provided a detailed comparison of the two in terms of accuracy, performance, and robustness. © Springer Nature Singapore Pte Ltd. 2018.Item Automatic text-independent Kannada dialect identification system(Springer Verlag service@springer.de, 2019) Chittaragi, N.B.; Limaye, A.; Chandana, N.T.; Annappa, B.; Koolagudi, S.G.This paper proposes a dialect identification system for the Kannada language. A system that can automatically identify the dialects of the language being spoken has a wide variety of applications. However, not many Automatic Speech Recognition (ASR) and dialect identification tasks are carried out in majority of the Indian languages. Further, there are only a few good quality annotated audio datasets available. In this paper, a new dataset for 5 spoken dialects of the Kannada language is introduced. Spectral and prosodic features have captured the most prominent features for recognition of Kannada dialects. Support Vector Machine (SVM) and neural networks algorithms are used for modeling text-independent recognition system. A neural network model that attempts for identification dialects based on sentence level cues has also been built. Hyper-parameters for SVM and neural network models are chosen using grid search. Neural network models have outperformed SVMs when complete utterances are considered. © Springer Nature Singapore Pte Ltd. 2019.Item Estimation of Tyre Pressure from the Characteristics of the Wheel: An Image Processing Approach(Springer, 2020) Vineeth Reddy, V.B.; Ananda Rao, H.; Yeshwanth, A.; Ramteke, P.B.; Koolagudi, S.G.Improper tyre pressure is a safety issue that falls prey to ignorance of users. But a drop in tyre pressure can result in the reduction of mileage, tyre life, vehicle safety and performance. In this paper, an approach is proposed to measure the tyre pressure from the image of the wheel. The tyre pressure is classified into under pressure and normal pressure using load index, tyre type, tyre position and ratio of compressed and uncompressed tyre radius. The efficiency of the feature is evaluated using three classifiers namely Random Forest, AdaBoost and Artificial Neural Networks. It is observed that the ratio of radii plays a major role in classifying the tyres. The proposed system can be used to obtain a rough idea on whether the tyre should be refilled or not. © 2020, Springer Nature Singapore Pte Ltd.Item Indian stock market prediction using deep learning(Institute of Electrical and Electronics Engineers Inc., 2020) Maiti, A.; Shetty D, P.In this paper, we predict the stock prices of five companies listed on India's National Stock Exchange (NSE) using two models- the Long Short Term Memory (LSTM) model and the Generative Adversarial Network (GAN) model with LSTM as the generator and a simple dense neural network as the discriminant. Both models take the online published historical stock-price data as input and produce the prediction of the closing price for the next trading day. To emulate the thought process of a real trader, our implementation applies the technique of rolling segmentation for the partition of training and testing dataset to examine the effect of different interval partitions on the prediction performance. © 2020 IEEE.Item Crack Detection in Concrete Using Artificial Neural Networks(Springer Science and Business Media Deutschland GmbH, 2023) Palanisamy, T.; Shakya, R.; Nalla, S.; Prakhya, S.S.This paper aims to explore the possibility of using machine learning (ML) algorithms and image processing to determine cracks in concrete and classify them as Cracked and Uncracked. This is a very current field of study with a lot of research currently taking place. In particular, neural network algorithms such as VGG16, ResNet50, Xception and MobileNet, were used to name a few. Two datasets were used to detect the presence of cracks in concrete. The first two datasets were taken from the Kaggle website. The first dataset is generated from 458 high-resolution images (4032 × 3024 pixels). This dataset consists of 40,000 images, 20,000 with and 20,000 without cracks. The second dataset had pictures of cracked and uncracked decks on a bridge from a dataset called SDNET2018 (2018). VGG16 Architecture based artificial neural network performed the best while MobileNet performed the worst. As the scope for the project expanded, an effort was made to determine crack properties, specifically crack width as an automated system for the same would be much more useful than a manual one. It was done using morphological transformations and concepts of Euclidean distance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Survey: Neural Network Authentication and Tampering Detection(Springer, 2023) Kumar, R.; P, A.; Naveen, B.; Chandavarkar, B.R.Neural networks have become quite the buzzword in a decade, resulting in extensive research and extensive integration of neural networks in application development. From self-driving vehicles to IoT devices, each such area has seen some form of integration of a neural network(s). Image and video content have found application in medical, forensic, etc. Due to the excessive use of digital content, there has also been a rise in various advanced image editing applications such as Photoshop, making it easier for people to tamper with images. Therefore, coming up with techniques to validate or authenticate images has gained much interest in recent times. Current neural network-based methods can see all kinds of tampering because neural network capability extracts complex features from the images, making them more effective. Thus, in this study, we review some image forgery techniques and look over how neural networks find their application to detect forgery and authenticate images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.