Faculty Publications
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Item Design and construction of BCH codes for enhancing data integrity in multi level flash memories(Inderscience Publishers, 2012) Rajesh Shetty, K.; Ramakrishna, K.; Prashantha Kumar, H.; Sripati, U.Flash memories have found extensive application for use in storage devices. The storage capacity and reliability of these devices have increased enormously over the years. With increase in density of data storage, the raw bit error rate (RBER), associated with the storage device increases. Error control coding (ECC) can be used to reduce the RBER to acceptable values so that these devices can be employed to store information in applications where data corruption is unacceptable. In this paper, we describe the synthesis of BCH codes for flash memories based on multi level cell (MLC) concept. This is in continuation of our work on synthesis of BCH codes for improving the performance of flash memories based on single level cells (SLC). The improvement in device integrity resulting from the use of these codes has been quantified in this paper along with computation of parameters which allows modelling of flash memory as an equivalent channel. While synthesising codes, we have adhered to the limitations imposed by the memory architecture. Use of these codes in storage devices will result in considerable enhancement of device reliability and consequently open up many new applications for this class of storage devices. © 2012 Inderscience Enterprises Ltd.Item Analysis of FSO Systems with SISO and MIMO Techniques(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Krishnan, P.Free space optics (FSO) is a form of line of sight communication technology that uses the help of LASERS and photodetectors to give optical connections from one place to another without the use of an optical fiber. The major hindrance to an FSO communication system comes in the form of atmospheric turbulences characterized by haze, rain, snow, storms among others. In this paper, the bit error rate performance of single input single output and multiple input multiple output based FSO system is analyzed and compared. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.Item Predicting ICD-9 code groups with fuzzy similarity based supervised multi-label classification of unstructured clinical nursing notes(Elsevier B.V., 2020) Gangavarapu, T.; Jayasimha, A.; S. Krishnan, G.S.; Kamath S?, S.In hospitals, caregivers are trained to chronicle the subtle changes in the clinical conditions of a patient at regular intervals, for enabling decision-making. Caregivers’ text-based clinical notes are a significant source of rich patient-specific data, that can facilitate effective clinical decision support, despite which, this treasure-trove of data remains largely unexplored for supporting the prediction of clinical outcomes. The application of sophisticated data modeling and prediction algorithms with greater computational capacity have made disease prediction from raw clinical notes a relevant problem. In this paper, we propose an approach based on vector space and topic modeling, to structure the raw clinical data by capturing the semantic information in the nursing notes. Fuzzy similarity based data cleansing approach was used to merge anomalous and redundant patient data. Furthermore, we utilize eight supervised multi-label classification models to facilitate disease (ICD-9 code group) prediction. We present an exhaustive comparative study to evaluate the performance of the proposed approaches using standard evaluation metrics. Experimental validation on MIMIC-III, an open database, underscored the superior performance of the proposed Term weighting of unstructured notes AGgregated using fuzzy Similarity (TAGS) model, which consistently outperformed the state-of-the-art structured data based approach by 7.79% in AUPRC and 1.24% in AUROC. © 2019 Elsevier B.V.Item Fuzzy uncertainty and its applications in reinforced concrete structures(Emerald Group Holdings Ltd., 2020) Worabo Woju, U.W.; Balu, A.S.Purpose: The aim of this paper is mainly to handle the fuzzy uncertainties present in structures appropriately. In general, uncertainties of variables are classified as aleatory and epistemic. The different sources of uncertainties in reinforced concrete structures include the randomness, mathematical models, physical models, environmental factors and gross errors. The effects of imprecise data in reinforced concrete structures are studied here by using fuzzy concepts. The aim of this paper is mainly to handle the uncertainties of variables with unclear boundaries. Design/methodology/approach: To achieve the intended objective, the reinforced concrete beam subjected to flexure and shear was designed as per Euro Code (EC2). Then, different design parameters such as corrosion parameters, material properties and empirical expressions of time-dependent material properties were identified through a thorough literature review. Findings: The fuzziness of variables was identified, and their membership functions were generated by using the heuristic method and drawn by MATLAB R2018a software. In addition to the identification of fuzziness of variables, the study further extended to design optimization of reinforced concrete structure by using fuzzy relation and fuzzy composition. Originality/value: In the design codes of the concrete structure, the concrete grades such as C16/20, C20/25, C25/30, C30/37 and so on are provided and being adopted for design in which the intermediate grades are not considered, but using fuzzy concepts the intermediate grades of concrete can be recognized by their respective degree of membership. In the design of reinforced concrete structure using fuzzy relation and composition methods, the optimum design is considered when the degree of membership tends to unity. In addition to design optimization, the level of structural performance evaluation can also be carried out by using fuzzy concepts. © 2020, Emerald Publishing Limited.Item How do open source app developers perceive API changes related to Android battery optimization? An empirical study(John Wiley and Sons Ltd, 2021) Marimuthu, C.; Chimalakonda, S.; Chandrasekaran, K.There is an increasing interest shown by researchers and developers in reducing the battery consumption of Android applications. Recently, the battery optimization features such as doze mode, app standby, background execution limits, and background location limits were introduced in the form of API changes. According to the API changes, application developers have to change their source code to manage the behavioral changes caused by operating system limitations. These battery optimization features are evolving rapidly, and the apps show unexpected behaviors until updating the source code. Also, developers find it difficult to cope with the changes. Therefore, there is a need to understand the behavioral changes, application developer's perceptions, and response patterns on the API changes to plan upcoming battery optimization features. In this article, we have collected the relevant GitHub issues from 225 open-source Android repositories and performed a thematic analysis of collected data. This study analyzes the 391 related issues to answer three research questions. This study's important finding is that developers often post issues related to delayed app notifications, inconsistent background location updates, and suspended background tasks, and so on. We found that library developers are showing a quick response to API changes compared with application developers. © 2020 John Wiley & Sons LtdItem Application of word embedding and machine learning in detecting phishing websites(Springer, 2022) Rao, R.S.; Umarekar, A.; Pais, A.R.Phishing is an attack whose aim is to gain personal information such as passwords, credit card details etc. from online users by deceiving them through fake websites, emails or any legitimate internet service. There exists many techniques to detect phishing sites such as third-party based techniques, source code based methods and URL based methods but still users are getting trapped into revealing their sensitive information. In this paper, we propose a new technique which detects phishing sites with word embeddings using plain text and domain specific text extracted from the source code. We applied various word embedding for the evaluation of our model using ensemble and multimodal approaches. From the experimental evaluation, we observed that multimodal with domain specific text achieved a significant accuracy of 99.34% with TPR of 99.59%, FPR of 0.93%, and MCC of 98.68% © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item Needle in a Haystack: Detecting Subtle Malicious Edits to Additive Manufacturing G-Code Files(Institute of Electrical and Electronics Engineers Inc., 2022) Beckwith, C.; Naicker, H.S.; Mehta, S.; Udupa, V.R.; Nim, N.T.; Gadre, V.; Pearce, H.; Mac, G.; Gupta, N.Increasing usage of digital manufacturing (DM) in safety-critical domains is increasing attention on the cybersecurity of the manufacturing process, as malicious third parties might aim to introduce defects in digital designs. In general, the DM process involves creating a digital object (as CAD files) before using a slicer program to convert the models into printing instructions (e.g., g-code) suitable for the target printer. As the g-code is an intermediate machine format, malicious edits may be difficult to detect, especially when the golden (original) models are not available to the manufacturer. In this work, we aim to quantify this hypothesis through a red team/blue team case study, whereby the red team aims to introduce subtle defects that would impact the properties (strengths) of the 3-D printed parts, and the blue team aims to detect these modifications in the absence of the golden models. The case study had two sets of models, the first with 180 designs (with two compromised using two methods) and the second with 4320 designs (with 60 compromised using six methods). Using statistical modeling and machine learning (ML), the blue team was able to detect all the compromises in the first set of data, and 50 of the compromises in the second. © 2009-2012 IEEE.Item Transfer learning based code-mixed part-of-speech tagging using character level representations for Indian languages(Springer Science and Business Media Deutschland GmbH, 2023) Anand Kumar, A.K.; Padannayil, S.K.Massive amounts of unstructured content have been generated day-by-day on social media platforms like Facebook, Twitter and blogs. Analyzing and extracting useful information from this vast amount of text content is a challenging process. Social media have currently provided extensive opportunities for researchers and practitioners to do adequate research on this area. Most of the text content in social media tend to be either in English or code-mixed regional languages. In a multilingual country like India, code-mixing is the usual fashion witnessed in social media discussions. Multilingual users frequently use Roman script, an convenient mode of expression, instead of the regional language script for posting messages on social media and often mix it with English into their native languages. Stylistic and grammatical irregularities are significant challenges in processing the code-mixed text using conventional methods. This paper explains the new word embedding via character level representation as features for POS tagging the code-mixed text in Indian languages using the ICON-2015, ICON-2016 NLP tools contest data set. The proposed word embedding features are context-appended, and the well-known Support Vector Machine (SVM) classifier has been used to train the system. We have combined the Facebook, Twitter, and WhatsApp code-mixed data of three Indian languages to train the Transfer learning based language-independent and source independent POS tagging. The experimental results demonstrated that the proposed transfer method achieved state-of-the-art accuracy in 12 systems out of 18 systems for the ICON data set. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item Shipping code towards data in an inter-region serverless environment to leverage latency(Springer, 2023) Sethi, B.; Addya, S.K.; Bhutada, J.; Ghosh, S.K.Serverless computing emerges as a new standard to build cloud applications, where developers write compact functions that respond to events in the cloud infrastructure. Several cloud service industries started adopting serverless for deploying their applications. But one key limitation in serverless computing is that it disregards the significance of data. In the age of big data, when applications run around a huge volume, to transfer data from the data side to the computation side to co-allocate the data and code, leads to high latency. All existing serverless architectures are based on the data shipping architecture. In this paper, we present an inter-region code shipping architecture for serverless, that enables the code to flow from computation side to the data side where the size of the code is negligible compared to the data size. We tested our proposed architecture over a real-time cloud platform Amazon Web Services with the integration of the Fission serverless tool. The evaluation of the proposed code shipping architecture shows for a data file size of 64 MB, the latency in the proposed code shipping architecture is 8.36 ms and in existing data shipped architecture is found to be 16.8 ms. Hence, the proposed architecture achieves a speedup of 2x on the round latency for high data sizes in a serverless environment. We define round latency to be the duration to read and write back the data in the storage. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item The Effect of Phrase Vector Embedding in Explainable Hierarchical Attention-Based Tamil Code-Mixed Hate Speech and Intent Detection(Institute of Electrical and Electronics Engineers Inc., 2024) Sharmila Devi, V.S.; Subramanian, S.; Anand Kumar, A.K.The substantial growth in social media users has led to a significant increase in code-mixed content on social media platforms. Millions of users on these platforms upload pictures and videos and post comments regarding their recent or exciting activities. Responding to this uploaded content, a few users occasionally use offensive language to insult others or specific groups. Social media platforms encounter challenges identifying and removing hate speech and objectionable content in various languages. Hate speech, in its general sense, refers to harmful posts directed at individuals or groups based on factors such as their sexuality, religion, community affiliation, disability, and others. Typically, offensive language is directly or indirectly utilized in hate speech posts to insult someone, causing psychological distress to users. In light of this, we propose developing a system to automatically block, remove, or report posts written in code-mixed Tamil containing hate speech. We have gathered code-mixed Tamil comments from Twitter and the Helo App, categorizing them as hate speech and classifying their intent. We have identified three categories of hate speech intent, namely Targeted Individual (TI), Targeted Group (TG), and Others (O). The Targeted Individual (TI) class encompasses posts aimed at a specific individual target. At the same time, the Targeted Group (TG) category primarily focuses on identifying people based on their religion, community, gender, and other characteristics. The Others (O) category encompasses untargeted offensive posts and other posts containing offensive language. In this context, we propose using a phrase-based, Explainable Hierarchical Attention model for hate speech detection. The results demonstrate that the proposed method is more effective in identifying and explaining hate speech and offensive language in social media posts. © 2013 IEEE.