Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Food classification from images using convolutional neural networks(Institute of Electrical and Electronics Engineers Inc., 2017) Attokaren, D.J.; Fernandes, I.G.; Sriram, A.; Vishnu Srinivasa Murthy, Y.V.; Koolagudi, S.G.The process of identifying food items from an image is quite an interesting field with various applications. Since food monitoring plays a leading role in health-related problems, it is becoming more essential in our day-to-day lives. In this paper, an approach has been presented to classify images of food using convolutional neural networks. Unlike the traditional artificial neural networks, convolutional neural networks have the capability of estimating the score function directly from image pixels. A 2D convolution layer has been utilised which creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. There are multiple such layers, and the outputs are concatenated at parts to form the final tensor of outputs. We also use the Max-Pooling function for the data, and the features extracted from this function are used to train the network. An accuracy of 86.97% for the classes of the FOOD-101 dataset is recognised using the proposed implementation. © 2017 IEEE.Item Classification of protein sequences by means of an ensemble classifier with an improved feature selection strategy(Springer Verlag, 2018) Sriram, A.; Sanapala, M.; Patel, R.; Patil, N.With decreasing cost of biological sequencing, the influx of new sequences into biological databases such as NCBI, SwissProt, UniProt is increasing at an ever-growing pace. Annotating these newly sequenced proteins will aid in ground breaking discoveries for developing novel drugs and potential therapies for diseases. Previous work in this field has harnessed the high computational power of modern machines to achieve good prediction quality but at the cost of high dimensionality. To address this disparity, we propose a novel word segmentation-based feature selection strategy to classify protein sequences using a highly condensed feature set. Using an incremental classifier selection strategy was seen to yield better results than all existing methods. The antioxidant protein data curated in the previous work was used in order to facilitate a level ground for evaluation and comparison of results. The proposed method was found to outperform all existing works on this data with an accuracy of 95%. © Springer Nature Singapore Pte Ltd. 2018.Item Genetic Algorithm-Based Optimization of Clustering Data Points by Propagating Probabilities(Springer Science and Business Media Deutschland GmbH, 2021) Dalmia, S.; Sriram, A.; Ashwin, T.S.Clustering is among the pivotal elementary operations in the field of data analysis. The efficiency of a clustering algorithm depends on a variety of factors like initialization of cluster centers, shape of clusters, density of the dataset, and complexity of the clustering mechanism. Previous work in clustering has managed to achieve great results but at the expense of a trial and error approach to achieve optimal values for user-defined parameters which have a huge bearing on the quality of the clusters formed. In this work, we propose a solution that optimizes the user-defined parameters for clustering algorithm called Probability Propagation (PP) by harnessing the capabilities of Genetic Algorithm (GA). In order to overcome this sensitivity in PP, a novel optimization technique is applied by obtaining the optimal values of δ and s using GA by maximizing inter-cluster spread and minimizing intra-cluster spread among the clusters being formed. The proposed method was found to retrieve top chromosomes (bandwidth and s) with a similar number of clusters, thus eliminating the sensitivity of user-defined parameters which is optimized by using GA. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Analyzing Banking Services Applicability Using Explainable Artificial Intelligence(Association for Computing Machinery, 2022) Sriram, A.; Gorti, S.S.; Amin, E.G.; Anand Kumar, M.A.Over the last few years, the banking sector has had a pivotal role to play in the global economy, comprising of about 24% of the global GDP and employing millions of people worldwide. Banks have a wide array of products and services to offer, ranging from ATMs, Tele-Banking, Credit Cards, Debit cards, Electronic Fund Transfers (EFT), Internet Banking, Mobile Banking, etc. Machine learning is a method of data analysis that automates analytical model building and can be an essential decision support tool for banks in providing services to certain customers and to help in improving customer satisfaction and experience based on collected data. In this study, we made use of several machine learning models and Artificial Neural Networks (ANN) to help banks make predictions about timely customer loan repayment and customer satisfaction. We explored different machine learning algorithms and have performed SHAP analysis, which has helped make conclusions about the significant features driving these decisions. © 2022 ACM.Item Exploring the Impact of External Factors on Ride-Hailing Demand: A Predictive Modelling Approach(The Society for the Study of Artificial Intelligence and Simulation of Behaviour, 2023) Sriram, A.; Ananthanarayana, V.S.This paper presents a comprehensive study on the usage of Uber in different markets, with a focus on understanding the impact of demographic factors, public transit proximity, weather and extreme events on the demand for Uber ride-hailing services. This study involves application of Explainable AI techniques for feature selection among multiple data sources to model external factors on the Uber ride usage. Furthermore, factors such as weather and local events are used for ride usage forecasting using spatiotemporal aspects and extreme event analysis. The results of this study showed that certain factors like demography, proximity of public transit play a role in shaping the usage patterns of Uber. Also, extreme events, such as weather conditions and local events, were found to have a significant impact on the demand for Uber services. This study provides valuable insights for Uber, similar ride-hailing services and policymakers for optimal resource allocation, and lays the foundation for further research on the relationship between transportation services and various contextual factors. © AISB Convention 2023.All rights reserved.