Showing papers by "Indian Institute of Technology Indore published in 2022"
TL;DR: In this paper, a new method for detecting COVID-19 and pneumonia using chest X-ray images was proposed, which can be described as a three-step process and achieved the highest testing classification accuracy of 96.6% using the VGG-19 model associated with the binary robust invariant scalable key-points (BRISK) algorithm.
76 citations
TL;DR: In this article, a brief review and perspective on the development of eutectic alloys is presented, emphasizing the emergence of complex microstructures during the solidification of ternary and higher-order multicomponent alloys.
52 citations
01 Jan 2022
TL;DR: In this paper, the authors discuss the importance of techno-economic assessment in the overall context of systems analysis, and the steps and tools to perform the analysis are discussed, including real options analysis.
Abstract: This chapter discuss the importance of techno-economic assessment in the overall context of systems analysis. The steps and tools to perform the analysis are discussed. Some of the recent developments in the field including real options analysis are discussed. An example of the approaches is presented.
38 citations
TL;DR: A highly efficient model named XSRU-IoMT, for effective and timely detection of sophisticated attack vectors in IoMT networks, developed using novel bidirectional simple recurrent units (SRU) using the phenomenon of skip connections to eradicate the vanishing gradient problem and achieve a fast training process in recurrent networks.
35 citations
TL;DR: In this paper, the EEG signals are first decomposed in sub-bands using empirical wavelet transform (EWT) based on the Fourier Bessel series expansion (FBSE) which is termed as FBSE-EWT.
30 citations
TL;DR: In this article, the authors used the concept of empirical wavelet transformation for preprocessing, selecting the best components of the red, green, and blue channels of the image are trained on the proposed network.
27 citations
TL;DR: In this article, a new strategy that integrates Long Short Term Memory (LSTM) models and reinforcement learning (RL) agents to forecast building next-day electricity consumption and peak electricity demand is presented.
22 citations
TL;DR: Compositional doping by nitrogen and sulfur into a carbon matrix with a distinct hollow sphere architecture was achieved via a simple approach and the co-doped carbon material was used as a bifunctional catalyst for an efficient CO 2 -epichlorohydrin cycloaddition reaction as discussed by the authors .
20 citations
TL;DR: In this paper , a DL model for all level feature extraction and fuzzy hyperplane based least square twin support vector machine (FLS-TWSVM) for the classification of the extracted features for early diagnosis of AD (FDN-ADNet) using extracted sagittal plane slices from 3D MRI images.
17 citations
TL;DR: In this paper , a 0°/90° basalt fiber-reinforced SiC micro and nano filled composites were tested for tensile, flexural, impact and hardness characteristics to determine the optimal composition.
Abstract: In recent years, basalt fiber reinforced polymer composites have gained popularity as an alternative to synthetic glass fiber reinforced polymer composites. Unidirectional 0°/90° basalt fiber-reinforced SiC micro and nano filled composites were tested for tensile, flexural, impact and hardness characteristics to determine the optimal composition. Comparing the mechanical properties of the composites reinforced various combinations of SiC micro or nano filler particles 4 wt% nano SiC filler addition exhibits superior properties. The scanning electron microscopy (SEM) images also reveals the improved interfacial interaction between the fiber mat and matrix for filler concentration up to 4 wt% SiC nanoparticles. Moreover, the wear resistance of the composites with 4 wt% SiC micro particles was better with low weight loss in wear test. The water absorption ability of the composite was low for the composites reinforced with 2 wt% SiC nanoparticles. Most of the properties of the composition with optimum filler addition are good enough for structural applications.
16 citations
TL;DR: In this paper , the authors used MODIS remote sensing satellite sensor based gross primary productivity (GPP) and remote sensing-based soil moisture data to compute the response of ecosystems to flash droughts in India.
Abstract: • The RI approach was used to understand the characteristics of flash droughts. • The study was carried out over 25 major river basins of India. • Investigates how the regional terrestrial carbon dynamics respond to flash droughts. • Additionally, the response of WUE to flash droughts was also investigated. Rapid onset droughts, termed as “flash droughts”, cause short-term but serious threats to terrestrial ecosystems and influence carbon dynamics due to insufficient warning. To date, how the regional terrestrial carbon dynamics respond to flash droughts in India remains unknown. Since, India is highly dependent on its cropland and vegetation, identifying the influence of flash droughts on terrestrial ecosystem is important. Here we use MODIS remote sensing satellite sensor based gross primary productivity (GPP) and remote sensing-based soil moisture data to compute the response of ecosystems to flash droughts in India. From the investigation, it was observed that GPP responds to more than 95% of the flash droughts across India, with the highest response frequency occurring over Ganga basin and southern India while the lowest response across northeastern India. The discrepancies in the response frequencies are mainly attributed to different vegetation resilience conditions across different parts of the country. Moreover, the mean response time is about 10 to 19 days averaged over India, with the lowest and highest response time over Indus-Ganga basins and northeastern Indian river basins (including the Brahmaputra, Minor rivers draining into Myanmar basin (MRMB), and Barak basins), respectively. Severe reduction in water use efficiency (WUE) was observed for the Ganga river basin and some parts of southern India, which highlighted the non-resilient nature of ecosystem towards rapid soil moisture variations. The study facilitates the identification of flash drought hotspots in the country including the Indus basin, Southern river basins (Cauveri, EFRPCP, and EFRSCB basins), some parts of the Ganga basin, and the ability of an ecosystem to withstand such drastic conditions. These findings highlight the need to adopt essential drought mitigation measures to safeguard the sustainability of ecosystems.
TL;DR: In this article , a nanocrystalline Pd/snO2 thin film was prepared on alumina substrate by reactive magnetron sputtering for highly sensitive and selective CO gas sensing.
TL;DR: The porphyrin-based D-A systems have been explored for its tunable optical and electronic properties, which can be attained by controlled modification of the substituents at the meso and β-pyrrolic positions as mentioned in this paper .
TL;DR: In this paper , the effect of morphological transformation from nanoparticles to ultrathin nanodiscs of MnTiO3 (MTO) perovskites as an efficient electrode for electrochemical supercapacitors was reported.
TL;DR: In this paper , a defect-engineered Mn-doped 2D monolayer MoS$_2$ (Mn-MoS$-2$) material where Mn was doped in the pristine MoS $_2/$ transition metal dichalcogenide (TMD) to activate the inert basal planes was investigated.
Abstract: Two-dimensional (2D) monolayer pristine MoS$_2$ transition metal dichalcogenide (TMD) is the most studied material because of its promising aspects as nonprecious electrocatalyst for hydrogen evolution reaction (HER). Previous studies have shown that the basal planes of the 2D MoS$_2$ are catalytically inert and hence, they cannot be used directly in desired applications such as electrochemical HER in industries. Here, we have thoroughly studied the defect-engineered Mn-doped 2D monolayer MoS$_2$ (Mn-MoS$_2$) material where Mn was doped in the pristine MoS$_2$ to activate the inert basal planes. Using density functional theory (DFT) method, we performed rigorous inspection of electronic structures and properties of the 2D monolayer Mn-MoS$_2$ to be a promising alternative to noble metal free catalysts for the effective HER. Periodic 2D slab of the monolayer Mn-MoS$_2$ was created to study the electronic properties and the reaction pathways occurring on the surface of the material. The detailed HER mechanism has been explored by creating the Mn$_1$Mo$_9$S$_{21}$ non-periodic finite molecular cluster model system using M06-L DFT method including solvation effects to determine the reaction barriers and kinetics. Our study reveals that the 2D Mn-MoS$_2$ follows the most favorable Volmer-Heyrovsky reaction mechanism with very low energy barriers during the H$_2$ evolution. It was found that the change of free energy barrier during the Heyrovsky reaction is about 10.34 - 10.79 kcal/mol, indicating an exceptional electrocatalyst for HER. The Tafel slope is lower in the case of 2D monolayer Mn-MoS$_2$ material due to the overlap of the s-orbital of the hydrogen and d-orbitals of the Mn atoms appeared in the HOMO and LUMO transition states (TS1 and TS2) of both the Volmer and Heyrovsky reaction steps.
TL;DR: In this article , a laser-based additive manufacturing method, the selective laser melting (SLM) technique, was used to fabricate the biomedical grade stainless steel 316L (316L SS) sample.
TL;DR: In this paper , a continuous depth version of the Residual Network (ResNet) called Neural Ordinary Differential Equations (NODE) was proposed for galaxy morphology classification.
TL;DR: In the proposed method, the empirical mode decomposition (EMD) method is applied to decompose the EMG signals into intrinsic mode functions (IMFs) and the suitable IMFs for feature selection are selected using the t-test based approach.
TL;DR: In this paper, a review highlights recent progress and developments in applying dendrimers for different immunoassays and their applicability in analyzing various biomarkers in clinical disease diagnosis.
TL;DR: A systematic literature review with state-of-the-art research on the application of parallel processing and shared/distributed techniques to determine communities for social network analysis is presented in this article.
Abstract: Community detection in social networks is the process of identifying the cohesive groups of similar nodes. Detection of these groups can be helpful in many applications, such as finding networks of protein interaction in biological networks, finding the users of similar mind for ads and suggestions, finding a shared research field in collaborative networks, analyzing public health, future link prediction in social networks, analyzing criminology, and many more. However, with the increase in the number of profiles and content shared on social media platforms, the analysis is often time-consuming and exhaustive. In order to speed up and optimize the community detection process, parallel processing and Shared/Distributed memory techniques are widely used. Despite community detection has widespread use in social networks, no attempt has ever been made to compile and systematically discuss research efforts on the emerging subject of identifying parallel and distributed methods for community detection in social networks. Most of the surveys described the serial algorithms used for community detection. Our survey work comes under the scope of new design techniques, exciting or novel applications, components or standards, and applications of an educational, transactional, and co-operational nature. This paper accommodates and presents a systematic literature review with state-of-the-art research on the application of parallel processing and Shared/Distributed techniques to determine communities for social network analysis. Advanced search strategy has been performed on several digital libraries for extracting several studies for the review. The systematic search landed in finding 3220 studies, among which 65 relevant studies are selected after conducting various screening phases for further review. The application of parallel computing, shared memory, and distributed memory on the existing community detection methodologies have been discussed thoroughly. More specifically, the central significance of this paper is that a systematic literature review is conducted to gather the relevant studies from different digital libraries and gray literature. Then, different parametric values of each selected study are appropriately compared. Moreover, the need for further research to speed up the process of community formation in parallel and shared approaches has been pinpointed more suitably. A pictorial glance of this paper is depicted as follows:
TL;DR: In this article , a review highlights recent progress and developments in applying dendrimers for different immunoassays and their applicability in analyzing various biomarkers in clinical disease diagnosis.
TL;DR: In this paper, an extensive experimental investigation is performed to obtain the frequency components of the test rig having the configuration of a modern wind turbine gearbox, and the experimental results present some new insight into the combined effect of AM and gravity.
TL;DR: A systematic literature review with state-of-the-art research on the application of parallel processing and shared/distributed techniques to determine communities for social network analysis is presented in this article .
Abstract: Community detection in social networks is the process of identifying the cohesive groups of similar nodes. Detection of these groups can be helpful in many applications, such as finding networks of protein interaction in biological networks, finding the users of similar mind for ads and suggestions, finding a shared research field in collaborative networks, analyzing public health, future link prediction in social networks, analyzing criminology, and many more. However, with the increase in the number of profiles and content shared on social media platforms, the analysis is often time-consuming and exhaustive. In order to speed up and optimize the community detection process, parallel processing and Shared/Distributed memory techniques are widely used. Despite community detection has widespread use in social networks, no attempt has ever been made to compile and systematically discuss research efforts on the emerging subject of identifying parallel and distributed methods for community detection in social networks. Most of the surveys described the serial algorithms used for community detection. Our survey work comes under the scope of new design techniques, exciting or novel applications, components or standards, and applications of an educational, transactional, and co-operational nature. This paper accommodates and presents a systematic literature review with state-of-the-art research on the application of parallel processing and Shared/Distributed techniques to determine communities for social network analysis. Advanced search strategy has been performed on several digital libraries for extracting several studies for the review. The systematic search landed in finding 3220 studies, among which 65 relevant studies are selected after conducting various screening phases for further review. The application of parallel computing, shared memory, and distributed memory on the existing community detection methodologies have been discussed thoroughly. More specifically, the central significance of this paper is that a systematic literature review is conducted to gather the relevant studies from different digital libraries and gray literature. Then, different parametric values of each selected study are appropriately compared. Moreover, the need for further research to speed up the process of community formation in parallel and shared approaches has been pinpointed more suitably. A pictorial glance of this paper is depicted as follows:
TL;DR: In this paper , the authors investigated the upshots of adaptive development of pure simplicial complexes (triad and tetrad) on the nature of the transition to desynchrony of the oscillator ensembles.
Abstract: Abstract This letter investigates the upshots of adaptive development of pure two- and three-simplicial complexes (triad and tetrad) on the nature of the transition to desynchrony of the oscillator ensembles. The adaptation exercised in the pure simplicial coupling takes a cue from the Hebbian learning rule, i.e., the coupling weight of a triad (tetrad) is prone to increase if the oscillators forming it are in phase and decrease if they are out of phase. The coupling weights in these pure simplicial complexes experiencing such adaptation give rise to first-order routes to desynchronization, whose onsets are entirely characterized by respective Hebbian learning parameters. Mean-field analyses presented for the order parameters for the adaptive two- and three-simplicial complexes strongly corroborate with the respective numerical assessments.
TL;DR: In this paper , the authors proposed two approaches known as oblique and rotation double random forests, which are multivariate decision trees with different transformations at each node for better diversity among the base learners.
TL;DR: In this article, the authors investigated the effect of substituting Cd2+ into Zn2+ lattice sites in CZTS nanocrystals (NCs) through a facile solution-based method, and the structural, morphological, optoelectronic, and power conversion efficiencies of the NCs synthesized have been systematically characterized using various experimental techniques.
TL;DR: In this article, the Triphala nanoparticles (TNp) was formulated, characterized and screened for its antioxidant, anti-diabetic, and antimicrobial potential and was incorporated into medical fabrics and evaluated for its antibacterial potential against E. coli ATCC (25922) and multi drug resistant (MDR) E coli isolated from diabetic wound.
TL;DR: In this paper , a co-doped ZnO nanorods (NRs) were prepared for photoelectrochemical water splitting (PEC) application, and the results of linear sweep voltammetry (LSV) showed that a photocurrent density of 1.87 mA/cm2 was achieved at 1.2 V vs Ag/AgCl for Sn0.05Al0.03Zn0.92O.
TL;DR: In this paper , the Triphala nanoparticles (TNp) was formulated, characterized and screened for its antioxidant, anti-diabetic, and antimicrobial potential and was incorporated into medical fabrics and evaluated for its antibacterial potential against E. coli ATCC (25922) and multi drug resistant (MDR) E coli isolated from diabetic wound.
TL;DR: In this paper , a detailed study on the local time, solar cycle, and geomagnetic dependencies of the ILG10 events are presented on local time and solar cycle dependencies.
Abstract: Geomagnetically induced current (GIC) measurements at the Mäntsälä, Finland (57.9° magnetic latitude) gas pipeline from 1999 through 2019 are analyzed. It is found that the GIC events with peak intensity A are not individual peaks, but occur in clusters with duration from ∼ 5 to ∼ 38 hours when GIC values are almost continuously above ∼ 1.5 A. The intense, long-duration GIC A clusters (ILG10) are characterized by average (median) duration of ∼ 17 ± 9 hours ( ∼ 14 hours), peak intensity of ∼ 21 ± 10 A ( ∼ 19 A), and time-integrated current flows of ∼ 1.0 ± 0.7 A-d ( ∼ 0.9 A-d) for all events under study. An one-to-one correlation is observed between the ILG10 events and intense substorm clusters characterized by average (median) duration of ∼ 20 ± 10 hours ( ∼ 17 hours), peak westward auroral electrojet intensity (presented by SuperMAG AL or SML index) of ∼ − 2238 ± 843 nT ( ∼ − 2099 nT) for all events. About 10 to 60 minutes fluctuations in the ILG10 events are found to be induced by substorm (SML) activity, and geomagnetic pulsations. A detailed study is presented on the local time, solar cycle, and geomagnetic dependencies of the ILG10 events. This will hopefully augment the predictability of the intense GICs.