Faculty Publications
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Item Graph representational learning for bandgap prediction in varied perovskite crystals(Elsevier B.V., 2021) Omprakash, P.; Manikandan, B.; Sandeep, A.; Shrivastava, R.; Viswesh, P.; Bhat Panemangalore, D.B.Perovskites are an important class of materials that are actively researched for applications in solar cells and other optoelectronic devices due to their ease of fabrication and tuneable bandgaps. High throughput computational techniques like Density Functional Theory (DFT) and Machine Learning (ML) are viable methods to accelerate discovery of new perovskite materials with favourable properties. ML specifically is faster and requires lesser computational power. We recognized the importance of having robust datasets for ML and hence collated a dataset of varied perovskite structures along with their indirect bandgaps. We employed a graph representational learning technique and trained a model that predicted bandgaps for all types of perovskites. The model has a mean absolute error of 0.28 eV and can predict bandgap in a few milliseconds. The metric of generalization gap is introduced to quantify the performance of ML models. This metric will help in building more generalized models that can predict properties for novel materials. Furthermore, we believe that these computational techniques should be user-friendly to those less experienced in the field. Hence, for researchers unacquainted with DFT or ML, we built a pipeline that abstracts the specific processes. This makes it easier for material scientists to quickly screen viable inorganic perovskite compounds allowing them to synthesize and experiment on the more promising compounds. © 2021 Elsevier B.V.Item Fog-Based Intelligent Machine Malfunction Monitoring System for Industry 4.0(IEEE Computer Society, 2021) Natesha, B.V.; Guddeti, R.M.R.There is an exponential increase in the use of Industrial Internet of Things (IIoT) devices for controlling and monitoring the machines in an automated manufacturing industry. Different temperature sensors, pressure sensors, audio sensors, and camera devices are used as IIoT devices for pipeline monitoring and machine operation control in the industrial environment. But, monitoring and identifying the machine malfunction in an industrial environment is a challenging task. In this article, we consider machines fault diagnosis based on their operating sound using the fog computing architecture in the industrial environment. The different computing units, such as industrial controller units or micro data center are used as the fog server in the industrial environment to analyze and classify the machine sounds as normal and abnormal. The linear prediction coefficients and Mel-frequency cepstral coefficients are extracted from the machine sound to develop and deploy supervised machine learning (ML) models on the fog server to monitor and identify the malfunctioning machines based on the operating sound. The experimental results show the performance of ML models for the machines sound recorded with different signal-to-noise ratio levels for normal and abnormal operations. © 2021 IEEE.Item AI Technology for NoC Performance Evaluation(Institute of Electrical and Electronics Engineers Inc., 2021) Bhowmik, B.; Hazarika, P.; Kale, P.; Jain, S.An on-chip network has become a powerful platform for solving complex and large-scale computation problems in the present decade. However, the performance of bus-based architectures, including an increasing number of IP cores in systems-on-chip (SoCs), does not meet the requirements of lower latencies and higher bandwidth for many applications. A network-on-chip (NoC) has become a prevalent solution to overcome the limitations. Performance analysis of NoC's is essential for its architectural design. NoC simulators traditionally investigate performance despite they are slow with varying architectural sizes. This work proposes a machine learning-based framework that evaluates NoC performance quickly. The proposed framework uses the linear regression method to predict different performance metrics by learning the trained dataset speedily and accurately. Varying architectural parameters conduct thorough experiments on a set of mesh NoCs. The experiments' highlights include the network latency, hop count, maximum switch, and channel power consumption as 30-80 cycles, 2-11, $25\mu \text{W}$ , and $240\mu \text{W}$ , respectively. Further, the proposed framework achieves accuracy up to 94% and speedup of up to $2228\times $. © 2004-2012 IEEE.Item Machine Learning for Vortex Flowmeter Design(Institute of Electrical and Electronics Engineers Inc., 2022) Thummar, D.; Reddy, Y.J.; Venugopal, V.Vortex flowmeters are one of the broadly used flow measurement devices in various industrial applications. The shape of the bluff body is the most critical parameter in the design of vortex flowmeter. The conventional approach of bluff body design relies on parametric shape optimization of a bluff body using experimentation and computational fluid dynamics simulations, which are expensive and time-consuming. In this study, we propose a novel machine learning (ML)-based approach to design bluff body shapes. Two ML models are developed using supervised ML using an artificial neural network (ANN). The first model predicts new optimum bluff body shapes for a given input flow characteristic. The second model predicts the deviation in Strouhal number for a given bluff body to determine its optimality. Data from the literature on the geometry of bluff bodies and fluid flow properties such as blockage ratio, Reynolds number, and Strouhal number are used for training ML models. The obtained ML results are in close agreement (±3.0%) compared with the computational fluid dynamics simulation results. This approach may find broad applicability for designing other fluid flowmeters. © 1963-2012 IEEE.Item Cardamom Plant Disease Detection Approach Using EfficientNetV2(Institute of Electrical and Electronics Engineers Inc., 2022) Sunil, C.K.; Jaidhar, C.D.; Patil, N.Cardamom is a queen of spices. It is indigenously grown in the evergreen forests of Karnataka, Kerala, Tamil Nadu, and the northeastern states of India. India is the third largest producer of cardamom. Plant diseases cause a catastrophic influence on food production safety; they reduce the eminence and quantum of agricultural products. Plant diseases may cause significantly high loss or no harvest in dreadful cases. Various diseases and pests affect the growth of cardamom plants at different stages and crop yields. This study concentrated on two diseases of cardamom plants, Colletotrichum Blight and Phyllosticta Leaf Spot of cardamom and three diseases of grape, Black Rot, ESCA, and Isariopsis Leaf Spot. Various methods have been proposed for plant disease detection, and deep learning has become the preferred method because of its spectacular accomplishment. In this study, U2-Net was used to remove the unwanted background of an input image by selecting multiscale features. This work proposes a cardamom plant disease detection approach using the EfficientNetV2 model. A comprehensive set of experiments was carried out to ascertain the performance of the proposed approach and compare it with other models such as EfficientNet and Convolutional Neural Network (CNN). The experimental results showed that the proposed approach achieved a detection accuracy of 98.26%. © 2013 IEEE.Item Classification of Phishing Email Using Word Embedding and Machine Learning Techniques(River Publishers, 2022) Somesha, M.; Pais, A.R.Email phishing is a cyber-attack, bringing substantial financial damage to corporate and commercial organizations. A phishing email is a special type of spamming, used to trick the user to disclose personal information to access his digital assets. Phishing attack is generally triggered by emailing links to spoofed websites that collect sensitive information. The APWG survey suggests that the existing countermeasures remain ineffective and insufficient for detecting phishing attacks. Hence there is a need for an efficient mechanism to detect phishing emails to provide better security against such attacks to the common user. The existing open-source data sets are limited in diversity, hence they do not capture the real picture of the attack. Hence there is a need for real-time input data set to design accurate email anti-phishing solutions. In the current work, it has been created a real-time in-house corpus of phishing and legitimate emails and proposed efficient techniques to detect phishing emails using a word embedding and machine learning algorithms. The proposed system uses only four email header-based heuristics for the classification of emails. The proposed word embedding cum machine learning framework comprises six word embedding techniques with five machine learning classifiers to evaluate the best performing combination. Among all six combinations, Random Forest consistently performed the best with FastText (CBOW) by achieving an accuracy of 99.50% with a false positive rate of 0.053%, TF-IDF achieved an accuracy of 99.39% with a false positive rate of 0.4% and Count Vectorizer achieved an accuracy of 99.18% with a false positive rate of 0.98% respectively for three datasets used. © 2022 River Publishers.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 Remote sensing and machine learning based framework for the assessment of spatio-temporal water quality in the Middle Ganga Basin(Springer Science and Business Media Deutschland GmbH, 2022) Krishnaraj, A.; Honnasiddaiah, R.Understanding the dynamics of water quality in any water body is vital for the sustainability of our water resources. Thus, investigating spatio-temporal changes of dominant water quality parameters (WQPs) in any study is indeed critical for proposing the appropriate treatment for the water bodies. Traditionally, concentrations of WQPs have been measured through intensive fieldwork. Additionally, many studies have attempted to retrieve concentrations of WQPs from satellite images using regression-based methods. However, the relationship between WQPs and satellite data is complex to be modeled accurately by using simple regression-based methods. Our study attempts to develop a machine learning model for mapping the concentrations of dominant optical and non-optical WQPs such as electrical conductivity (EC), pH, temperature (Temp), total dissolved solids (TDS), silicon dioxide (SiO2), and dissolved oxygen (DO). In this context, a remote sensing framework based on the extreme gradient boosting (XGBoost) and multi-layer perceptron (MLP) regressor with optimized hyper parameters (HPs) to quantify concentrations of different WQPs from the Landsat-8 satellite imagery is developed. We evaluated six years of satellite data stretching spatially from upstream to downstream Ankinghat to Chopan (20 stations under Central Water Commission (CWC), Middle Ganga Basin) for characterizing the trends of dominant physico-chemical WQPs across the four clusters identified in our previous study. Through the developed XGBoost and MLP regression models between measured WQPs and the reflectance of the pixels corresponding to the sampling stations, a significant coefficient of determination (R2) in the range of 0.88–0.98 for XGBoost and 0.72–0.97 for MLP were generated, with bands B1–B4 and their ratios more consistent. Indeed, these findings indicate that from a small number of in-situ measurements, we can develop reliable models to estimate the spatio-temporal variations of physico-chemical and biological WQPs. Therefore, models generated from Landsat-8 could facilitate the environmental, economic, and social management of any waterbody. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item Effect of dry torrefaction pretreatment of the microwave-assisted catalytic pyrolysis of biomass using the machine learning approach(Elsevier Ltd, 2022) Ramesh, R.; Suriapparao, D.V.; Sankar Rao, C.S.; Sridevi, V.; Kumar, A.This study employs the Leave-One-Out cross-validation approach to build a machine-learning model using polynomial regression to predict pyro product yield through microwave-assisted pyrolysis of sawdust over KOH catalyst and graphite powder a susceptor. The determination of coefficient (R2) validates the developed models. All the developed models achieved a high prediction accuracy with R2 > 0.93, which signifies that the experimental values are in good agreement with the predicted one. The dependence of the catalyst loading and pretreatment temperature on dominating process parameters such as heating rate, pyrolysis temperature, susceptor thermal energy, and pyro products, namely bio-oil, biochar, and biogas, are explored. The yield of biochar is reduced; however, bio-oil and biogas are enhanced as the catalyst loading increased. On the other hand, increasing the temperature of pretreated sawdust decreased bio-oil and biogas yields while increasing biochar yields. Further, microwave conversion efficiency, and susceptor thermal energy increased with increased catalyst quantity and pretreatment temperatures of sawdust. It was observed that the average heating rate was increased by increasing the catalyst quantity while maintaining the same pyrolysis time until pretreatment temperatures of 150 °C were reached, after which the heating rate dropped due to the continuous microwave energy input to the system. © 2022 Elsevier LtdItem Microwave-assisted in-situ catalytic pyrolysis of polystyrene: Analysis of product formation and energy consumption using machine learning approach(Institution of Chemical Engineers, 2022) Terapalli, A.; Kamireddi, D.; Sridevi, V.; Tukarambai, M.; Suriapparao, D.V.; Sankar Rao, C.S.; Gautam, R.; Modi, P.R.Microwave-assisted catalytic pyrolysis is a prominent technology for the production of high-quality fuel intermediates and value-added chemicals from polystyrene waste. The objectives of this study were to understand the role of catalyst (KOH) on polystyrene (PS) pyrolysis. Pyrolysis experiments were conducted using a microwave oven at a power of 450 W and a temperature of 600 °C. Graphite susceptor (10 g) was used to achieve the required pyrolysis conditions. In addition, the design of experiments (DoE) with machine learning (ML) was used to understand the loading of PS (5 g, 27.5 g, and 50 g), and KOH (5 g, 7.5 g, and 10 g). The products including oil, gas, and char were collected in every experiment. The average heating rates achieved were in the range of 30–50 °C/min. The specific microwave power (microwave power per unit mass of feedstock) decreased with an increase in PS amount from 90 to 9 W/g. However, the specific microwave energy (microwave energy per unit mass of feedstock) (27–73 kJ/g) was in line with the average heating rate. The maximum yield of pyrolysis oil was found to be 95 wt%, which was obtained with a PS:KOH ratio of 27.5 g: 7.5 g. The oil yield increased from 80 to 95 wt% when the mass of the catalyst increased from 5 to 7.5 g. On the other hand, the gas yield (3–18 wt%) varied significantly and char yield (1–2 wt%) was not influenced. The yields predicted by ML matched well with the experimental yields. This study demonstrated the potential of KOH as a catalyst for PS pyrolysis technology as the formation of aliphatic hydrocarbons in the oil fraction was significantly promoted. © 2022 The Institution of Chemical Engineers