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Optimized deep maxout for breast cancer detection: consideration of pre-treatment and in-treatment aspect
Breast cancer is one of the deadliest diseases, accounting for the second-highest rate of cancer mortality among females. Breast tissue begins to develop cancerous, malignant lumps as the disease progresses. Self-examinations and routine clinical checks aid in early diagnosis, which considerably increases the likelihood of survival. Because of this, we have created a revolutionary method for finding breast cancer that has the following four steps. Fuzzy filters are used in the initial pre-processing stage to reduce noise and improve outcomes from the incoming data. In the second stage, we have presented an Improved Hierarchical DBSCAN (Density-based clustering algorithm) for the segmentation of anomalous areas. Feature extraction will be carried out following segmentation. We have also developed a better kurtosis-based feature to complement traditional statistical and shape-based features and deliver better results. The Optimized Deep Maxout Neural Network is used for classification in the final step, with the suggested Shark Smell Indulged Shuffled Shepherd Optimization used to optimize the weight parameter (SSISSO). At 90% the learning percentage of the proposed model SSISSO model has achieved 0.984391 accuracy, which is superior to 22.54%, 28.46%, 17.44%, 17%, 15.04%, 13.28%, 29.45%, 28.59%, 21.58%, and 30.72% as compared to other methods like SVM-BS1, CNN-BS7, LSTM, NN, Bi-GRU, RNN, ARCHO, AOA, HGS, CMBO, SSOA, and SSO. Finally, the results of the proposed breast cancer detection technique are compared with conventional techniques. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Synergistic effects of CuO/TiO2-chitosan-farnesol nanocomposites: Synthesis, characterization, antimicrobial, and anticancer activities on melanoma cells SK-MEL-3
The current investigation focuses on synthesizing copper oxide (CuO)-titanium oxide (TiO2)-chitosan-farnesol nanocomposites with potential antibacterial, antifungal, and anticancer properties against Melanoma cells (melanoma cells [SK-MEL-3]). The nanocomposites were synthesized using the standard acetic acid method and subsequently characterized using an X-ray diffractometer, scanning electron microscope, transmission electron microscopy, and Fourier transform infrared spectroscopy. The results from the antibacterial tests against Streptococcus pneumoniae and Stapylococcus aureus demonstrated significant antibacterial efficacy. Additionally, the antifungal studies using Candida albicans through the agar diffusion method displayed a considerable antifungal effect. For evaluating the anticancer activity, various assays such as MTT assay, acridine orange/ethidium bromide dual staining assay, reactive oxygen species (ROS) generation assay, and mitochondrial membrane potential (MMP) analysis were conducted on SK-MEL-3 cells. The nanocomposites exhibited the ability to induce ROS generation, decrease MMP levels, and trigger apoptosis in SK-MEL-3 cells. Collectively, the findings demonstrated a distinct pattern for the synthesized bimetallic nanocomposites. Furthermore, these nanocomposites also displayed significant (p < 0.05) antibacterial, antifungal, and anticancer effects when tested on the SK-MEL-3 cell line. 2023 Wiley-VCH GmbH. -
Magnetically retractable tea extract stabilized palladium nanoparticles for denitrogenative cross-coupling of aryl bromides with arylhydrazines under green conditions: An alternate route for the biaryls synthesis
Novel palladium based magnetic nanocatalyst was synthesized by the co-precipitation method and coated with silica and tea extract as stabilizing agent. Palladation onto the prepared nanocomposite was done to get ION-SiO2/TE-Pd(0) catalyst. Our study is one of the limited number of studies reported for the catalytic denitrogenative coupling of arylbromide and arylhydrazine. This led to the construction of important substituted biaryls bearing various substituents with 8292% yields. The synthesized nanocatalyst was characterized using structural and morphological characterization techniques. It was also observed that only 2 mol% of ION-SiO2/TE-Pd(0) catalyst was sufficient for the catalysis and reusable upto six cycles. 2024 The Authors -
Implementation of survivability aware protocols in WSN for IoT applications using Contiki-OS and hardware testbed evaluation
The Internet of Things is a network of devices capable of operating and communicating individually and working for a specific goal collectively. Technologically, many networking and computing mechanisms have to work together with a common objective for the IoT applications to function, and many sensing and actuating devices have to get connected to the Internet backbone. The networks of resource-constrained sensor devices constitute an integral part of IoT application networks. Network survivability is a critical aspect to consider in the case of a network of low-power, resource-constrained devices. Algorithms at different layers of the protocol stack have to work collectively to enhance the survivability of the application network. In this article, the survivability-aware protocols for wireless sensor networks for IoT applications are implemented in real network scenarios. The routing strategy, Survivable Path Routing protocol, and the channel allocation technique, Survivability Aware Channel Allocation, are implemented in Contiki-OS, the open-source operating system for IoT. Furthermore, the implementation scenarios are tested with the FIT IoT Lab hardware testbed. Simulated results are compared with the results obtained from the testbed evaluation. 2023 Elsevier B.V. -
Reinforcement learning strategies using Monte-Carlo to solve the blackjack problem
Blackjack is a classic casino game in which the player attempts to outsmart the dealer by drawing a combination of cards with face values that add up to just under or equal to 21 but are more incredible than the hand of the dealer he manages to come up with. This study considers a simplified variation of blackjack, which has a dealer and plays no active role after the first two draws. A different game regime will be modeled for everyone to ten multiples of the conventional 52-card deck. Irrespective of the number of standard decks utilized, the game is played as a randomized discrete-time process. For determining the optimum course of action in terms of policy, we teach an agent-a decision maker-to optimize across the decision space of the game, considering the procedure as a finite Markov decision chain. To choose the most effective course of action, we mainly research Monte Carlo-based reinforcement learning approaches and compare them with q-learning, dynamic programming, and temporal difference. The performance of the distinct model-free policy iteration techniques is presented in this study, framing the game as a reinforcement learning problem. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
A weighted-Weibull distribution: Properties and applications
The paper describes a two parameter model and its relationship to the widely used Weibull model. Mathematical properties of the distribution like survival and hazard functions, moments, harmonic and geometric means, Shannon entropy and mean residual life are derived. Different methods of estimation are discussed and a simulation study is performed to verify the efficiency of estimation methods. Applications of our distribution in different scenarios observed in real life areillustrated. 2023 John Wiley & Sons Ltd. -
RF-ShCNN: A combination of two deep models for tumor detection in brain using MRI
The tumor in the brain is the reason for jagged cell enlargement in the brain. Magnetic resonance imaging (MRI) is a common scheme to identify tumor existence in the brain. With these MRIs, the medical practitioner can examine and detect the abnormal growth of tissues and corroborate if the brain is influenced by a tumor or not. Due to the appearance of artificial intelligence models, the discovery of brain tumor is performed by adapting different models which thereby help in making decisions and selecting the most suitable diagnosis for patients. The main motivation of this work is to reduce the death rate. If they are not adequately treated, the survival rate of the patient decreases. The correct diagnoses help patients receive accurate treatments and survive for a long time. This paper develops a hybrid model, namely the Residual fused Shepherd convolution neural network (RF-ShCNN) for discovering tumor in the brain considering MRI. Thus, the Adaptive wiener filtering is adapted to filter image-commencing noise. Thereafter, Conditional Random Fields-Recurrent Neural Networks (CRF-RNN) are adapted for segmentation followed by the mining of essential features. Lastly, the features employed in RF-ShCNN for making effective brain tumor detection by means of MRI. Thus, the RF-ShCNN is built by unifying the deep residual network and Shepherd convolution neural network. The hybridization is done by adding a regression layer wherein the regression is fused with Fractional calculus (FC) to make effective detection. The RF-ShCNN provided better accuracy of 94%, sensitivity of 95% and specificity of 94.9%. 2023 -
Process of Emotion Regulation in Indian Couples During Gottmans Dreams-Within-Conflict Intervention: A Mixed-Methods Design Study
Gottman Couple Therapy (GCT) is based on 40 + years of empirical findings and advocates process research, enabling an understanding of how an intervention works. Dreams-within-Conflict (DWC) is a GCT technique that softens the stand on unresolvable issues by facilitating positive emotion regulation strategies such as expressing vulnerabilities, understanding, and soothing in place of destructive strategiessuch as criticism and defensiveness. The aim of the study is to understand the emotion regulation process during a one-session DWC intervention using a convergent parallel mixed-methods design examining N = 30 individuals (15 couples) during the DWC intervention. The changes in emotion regulation strategies (Extrinsic/Intrinsic affect Worsening/Improving strategiesEW, IW, EI, II) in partners were examined in the presence of individual characteristics of emotion regulation traits (cognitive-reappraisal and suppression) and beliefs using self-assessment questionnaires, feedback reports, thematic coding of video recordings, and a semi-structured interview. Paired-samplest-test results showed that DWC fosters emotion regulation strategies by significantly decreasing partners EW and increasing EI and II strategies. Though IW strategies declined during-DWC, the changes were not significant. Hierarchical linear modeling findings showed that before-DWC emotion regulation strategies, gender, and individual emotion regulation traits of cognitive-reappraisal and suppression predicted EI, and before-DWC strategies predicted II, but none of the variables predicted EW and IW during-DWC. To further understand the interventional implications, the emotional regulation strategies and preferences for expression (over suppression) shared by the Indian couples were examined using thematic analysis. The results show that avoidance, conflict behaviors, and prioritizing parents emotions over partners (in men) were the most often employed regulatory strategies. Simultaneously, Indian couples unanimously agreed that expression of emotions was a crucial factor for marital satisfaction. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Assessing Land Use Transformation in Kanhangad Town: A Special Emphasis on Wetland Ecosystems
Kerala, renowned for its lush landscapes, is facing environmental challenges due to rapid urbanization, particularly in Kanhangad. This area, notable for its unique wetland ecosystem crucial for biodiversity and human livelihoods, is experiencing a conflict between residential development and wetland conservation. A comprehensive study in Kanhangad, employing diverse data sources such as open-source data, Google Earth Satellite Imagery, OpenStreetMap, and tools like ArcGIS, provides a detailed analysis of land use and its environmental impacts. The study combines digital data analysis with physical surveys to understand the ecological and developmental status comprehensively. The study reveals a dominant trend in Kanhangad's land use, with residential areas comprising 52% of the total land, mostly large, detached single-family homes. This reflects a societal shift towards viewing homes as status symbols, contributing to natural resource depletion. The research underscores the need for sustainable, low-cost housing, suggesting vertical housing as a potential solution to balance residential demands with environmental conservation. Kanhangad's wetlands, essential for local biodiversity and livelihoods, face threats from urban development and infrastructural expansion. The study shows a drastic reduction in wetland area, from 12.9 km in 2004-05 to just 1.66 km by 2020-21, indicating severe ecological degradation. Despite the Kerala Conservation of Paddy land and Wetland Act of 2008, which aims to protect these ecosystems, its limited effectiveness is evident from the ongoing depletion of wetlands. This situation calls for stricter enforcement of environmental regulations and greater public involvement in conservation efforts. Furthermore, the research examines the Kerala Paddy and Wetland Conservation Act-2008, analysing its role and effectiveness in local environmental governance. The Act, focusing on prohibiting wetland and paddy land conversion, is vital for regional conservation. However, gaps in its implementation are highlighted, especially considering the exacerbation of the 2018 and 2019 Kerala floods due to land conversion practices. The study emphasizes the urgent need for more robust environmental protection measures. 2024 by authors. All rights reserved. -
Comparative electrochemical investigation for scheelite structured metals tungstate (MWO4 (M = Ni, Cu and Co)) nanocubes for high dense supercapacitors application
Scheelite structured metal tungstate MWO4 (M = Ni, Cu and Co) nanocubes were synthesized through the chemical reflux for supercapacitors application and ceyltrimethylammonium bromide (C-TAB) as surfactant. In X-ray diffraction (XRD) result are fit with relevant JCPDS cards, synthesized materials are closely matched with monoclinic and triclinic crystal phase corresponding to NiWO4, CoWO4 and CuWO4 with Scheelite type structure. To resist the growth of the particles and succeeding nanocubes morphology were achieving by using PEG-400 and C-TAB act as a surfactant. The prepared modified electrodes were examined electrochemical analysis after successive coating of working material in empty Ni foil. From the galvanostatic charge-discharge (GCD) comparative analysis, fast ions movements are interacts through the aqueous electrolyte medium with nanocubes NiWO4 electrode are achieving specific capacitance of 1185 Fg?1 at 0.5 Ag?1 and cyclic stability 93.084 % (retentivity) formerly compare to CuWO4 and CoWO4 electrodes. 2023 -
A Progressive UNDML Framework Model for Breast Cancer Diagnosis and Classification; [Un modelo marco progresivo UNDML para el diagntico y clasificaci del ccer de mama]
According to recent research, it is studied that the second most common cause of death for women worldwide is breast cancer. Since it can be incredibly difficult to determine the true cause of breast cancer, early diagnosis is crucial to lowering the diseases fatality rate. Early cancer detection raises the chance of survival by up to 8 %. Radiologists look for irregularities in breast images collected from mammograms, X-rays, or MRI scans. Radiologists of all levels struggle to identify features like lumps, masses, and micro-calcifications, which leads to high false-positive and false-negative rates. Recent developments in deep learning and image processing give rise to some optimism for the creation of improved applications for the early diagnosis of breast cancer. A methodological study was carried out in which a new Deep U-Net Segmentation based Convolutional Neural Network, named UNDML framework is developed for identifying and categorizing breast anomalies. This framework involves the operations of preprocessing, quality enhancement, feature extraction, segmentation, and classification. Preprocessing is carried out in this case to enhance the quality of the breast picture input. Consequently, the Deep U-net segmentation methodology is applied to accurately segment the breast image for improving the cancer detection rate. Finally, the CNN mechanism is utilized to categorize the class of breast cancer. To validate the performance of this method, an extensive simulation and comparative analysis have been performed in this work. The obtained results demonstrate that the UNDML mechanism outperforms the other models with increased tumor detection rate and accuracy. 2024; Los autores. -
Navigating brand equity in personal care: Examining the influence of direct-to-consumer brands and the mediating power of brand image
The COVID-19 pandemic triggered consumers to buy products online, leading t o unprecedented and unforeseen growth in the e-commerce sector. Therefore, the revolution made by Direct-to-consumer (DTC) brands went unnoticed. Unlike the conventional approach, which took years to build brand trust and equity, the DTC business model allows companies to grow exponentially with their presence in personal care online marketplaces. Therefore, the rise of DTC brands empowers small and medium enterprises (SMEs) and micro small and medium enterprises (MSMEs) in India, mainly because the return on ad spend (ROAS) for these brands is a huge problem when compared to giant companies. Also, branding in tier 2 and tier 3 markets has triggered new hurdles as consumers need to build brand trust to pay or transact online. The present research examines how a DTC website and electronic word-of-mouth (eWOM) can enhance the effectiveness of branding for direct-to-consumer brands. The study employed a quantitative methodology by analyzing the survey-based research design, with 389 respondents who were aware of and/or used DTC personal care brands in India participating. The present studys findings demonstrate that website attractiveness and electronic word-of-mouth enhance DTC brands and minimize costs developed to advertise a product, increasing ROAS. Further research studies would broaden DTC brands' knowledge by investigating the impact of e-commerce and social media channels on enhancing consumers' brand equity or purchase intention. 2024 Conscientia Beam. All Rights Reserved. -
Spectral and temporal features of GX 13+1 as revealed by AstroSat
GX 13+1, a neutron star low-mass X-ray binary that exhibits the properties of both atoll and Z sources, is studied using data from Soft X-ray Telescope and Large Area X-ray Proportional Counter (LAXPC) onboard AstroSat. The source traces a ? shaped track in its hardness-intensity diagram (HID). Spectral modelling of the data in the 0.7-30.0 keV energy range, with the model-+, yields orbital inclination angle (?) of 77. Flux resolved spectral analysis reveals the ? shaped pattern in the plots of spectral parameters kTe, kTbb, and ? versus Fbol, closely resembling the pattern traced in LAXPC HID. This indicates changes in the spectral properties of the corona and the boundary layer/accretion disc. Assuming that the accretion disc truncates at the AlfvCrossed D sign n radius, the upper limit of the magnetic field strength (B) at the poles of neutron star in GX 13+1 is calculated to be 5.10 108 G (for kA = 1 and ? = 0.1), which is close to that of atoll sources. Furthermore, thickness of the boundary layer is estimated to be 5.70 km, which results in the neutron star radius value of 14.50 km. Quasi-periodic oscillations (QPOs) at 56 4 and 54 4 Hz are detected in Regions D and E of HID, respectively. The frequencies of these QPOs are similar to the characteristic frequency of horizontal branch oscillation and these do not exhibit a positive correlation with mass accretion rate. -
Probing star formation in five of the most massive spiral galaxies observed through ASTROSAT UltraViolet Imaging Telescope
We present highly resolved and sensitive imaging of the five nearby massive spiral galaxies (with rotation velocities > 300 km s?1) observed by the UltraViolet Imaging Telescope onboard Indias multiwavelength astronomy satellite ASTROSAT, along with other archival observations. These massive spirals show a far-ultraviolet star formation rate in the range of ? 1.4 13.7 M? yr?1 and fall in the Green Valley region with a specific star formation rate within ? 10?11.5 10?10.5 yr?1. Moreover, the mean star formation rate density of the highly resolved star-forming clumps of these objects is in the range 0.011 0.098 M? yr?1 kpc?2, signifying localized star formation. From the spectral energy distributions, under the assumption of a delayed star formation model, we show that the star formation of these objects had peaked in the period of ? 0.8 2.8 Gyr after the Big Bang and the object that has experienced the peak sooner after the Big Bang show relatively less star-forming activity at z ? 0 and falls below the main-sequence relation for a stellar content of ? 1011 M?. We also show that these objects accumulated much of their stellar mass in the early period of evolution with ? 31 42 per cent of the total stellar mass obtained in a time of (1/16) (1/5)th the age of the Universe. We estimate that these massive objects convert their halo baryons into stars with efficiencies falling between ? 7 and 31 per cent. 2024 Oxford University Press. All rights reserved. -
Development of CeO2ZrO2 bimetallic oxide catalyst for quinoxaline synthesis
In recent years, heterogeneous catalysts have led to environment-friendly transformations with better yields and reusability. Pd, one of the initial metals employed in heterogeneous organic synthesis, suffered from limitations like its high cost. This justifies the need for development of catalysts with abundant, low-cost metals, which has been receiving a lot of attention in the scientific community. In this work, a bimetallic oxide catalyst, CeO2ZrO2, is synthesized by a solgel route. The structure and morphology of the catalyst are investigated using X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray analysis, thermogravimetric analysis, BrunauerEmmettTeller measurements, and temperature-programmed desorption. It is utilized for obtaining quinoxaline derivatives at room temperature. 2,3-Diphenylquinoxaline is obtained via a simple condensation reaction between 1,2-diaminobenzene and 1,2-diketones, catalyzed by CeO2(50)ZrO2(50) with 87% yield in 15 min. Quinoxalines are known for their biological and therapeutic activities; hence, they are essential molecules. The biological activity of the synthesized quinoxaline derivatives has been evaluated against bacterial and fungal strains. 2023 Society of Chemical Industry. 2023 Society of Chemical Industry. -
Investigation of the correlation between optical and ?-ray flux variations in the blazar Ton 599
The correlation between optical and ?-ray flux variations in blazars reveals a complex behaviour. In this study, we present our analysis of the connection between changes in optical and ?-ray emissions in the blazar Ton 599 over a span of approximately 15 yr, from 2008 August to 2023 March. Ton 599 reached its highest flux state across the entire electromagnetic spectrum during the second week of 2023 January. To investigate the connection between changes in optical and ?-ray flux, we have designated five specific time periods, labelled as epochs A, B, C, D, and E. During periods B, C, D, and E, the source exhibited optical flares, while it was in its quiescent state during period A. The ?-ray counterparts to these optical flares are present during periods B, C, and E; however, during period D, the ?-ray counterpart is either weak or absent. We conducted a broad-band spectral energy distribution (SED) fitting by employing a one-zone leptonic emission model for these epochs. The SED analysis unveiled that the optical-ultraviolet emission primarily emanated from the accretion disc in quiescent period A, whereas synchrotron radiation from the jet dominated during periods B, C, D, and E. Diverse correlated patterns in the variations of optical and ?-ray emissions, like correlated optical and ?-ray flares, could be accounted for by changes in factors such as the magnetic field, bulk Lorentz factor, and electron density. On the other hand, an orphan optical flare could result from increased magnetic field and bulk Lorentz factor. 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
DEVELOPMENT AND EVALUATION OF PNEUMFC NET: A NOVEL AUTOMATED LIGHTWEIGHT FULLY CONVOLUTIONAL NEURAL NETWORK MODEL FOR PNEUMONIA DETECTION
The aim of this study is to address the challenges of pneumonia diagnosis under constraint resources and the need for quick decision making. We present the PneumFC Net, a novel architectural solution where our approach focuses on minimizing the number of trainable parameters by incorporating transition blocks that efficiently manage channel dimensions and reduce number of channels. In contrast to using fully connected layers, which disregard the spatial structure of feature maps and substantially increase parameter counts, we exclusively employ only convolutional layer approach. In the study, X-ray image dataset is used to train and evaluate the proposed Convolutional Neural Network model. By carefully designing the architecture, the model achieves a balance between parameters and accuracy while maintaining comparable performance to pre-trained models. The results demonstrate the model's effectiveness in detecting pneumonia images reliably. In addition, the study examines the decision-making process of the model using Grad-CAM, which helps to identify important aspects of radiographic images that contribute to the positive pneumonia prediction. Furthermore, the study shows that the proposed model, Pneum FC Net not only has the highest accuracy of 98%, but the total trainable model parameters is only 0.02% of the next best model VGG-16, thus establishing the potential of this new robust Deep Learning model. This research primarily addresses concerns related to mitigating significant computational requirements, with a specific focus on implementing lightweight networks. The contribution of this work involves the development of resource-efficient and scalable solution for pneumonia detection. 2024 Little Lion Scientific. All rights reserved. -
Assessment of diversity, abundance, and seasonal variatons of bird species in Bengaluru District, India during COVID-19 lockdown
The study investgates bird populaton dynamics in Bengaluru, India, post-lockdown, focusing on occurrence, seasonal abundance, species diversity, richness, dominance, and evenness. It covers 55 bird species across 52 genera, grouped into 32 families within 13 orders, with a notable peak in winter. Various indices, including Shannon Wiener, Margalefs, Pielous, and Simpsons, reveal signifcant seasonal diferences in bird populaton characteristcs. The Rock Pigeon Columba livia dominates, while the Black-headed Ibis Threskiornis melanocephalus is less prevalent. The study identfes Near Threatened species like Black-headed Ibis and Oriental Darter Anhinga melanogaster, along with Least Concern species per the IUCN Red List. Common species include Rock Pigeon, Large-billed Crow Corvus macrorhynchos, House Crow Corvus splendens, Black Drongo Dicrurus macrocercus, Brown Shrike Lanius cristatus, Common Myna Acridotheres trists, Jungle Myna Acridotheres fuscus, Red-whiskered Bulbul Pycnonotus jocosus, and Streak-throated Swallow Petrochelidon fuvicola. The study aims to inform improved management and conservaton strategies for Bengalurus diverse bird species. Hemanth et al. 2024. Creatve Commons Atributon 4.0 Internatonal License. JoTT allows unrestricted use, reproducton, and distributon of this artcle in any medium by providing adequate credit to the author(s) and the source of publicaton -
Fear of COVID-19, workplace phobia, workplace deviance and perceived organizational support: A moderated mediation model
This paper aims to test a moderated-mediation model examining therelationships between Fear of COVID-19, workplace phobia, work deviance behaviourand perceived organizational support among hotel employees. An online questionnaire was administered to collect data, to which 481 responded. Data was collected from full-time frontline employees working in the Maldivian hospitality industry. The moderated-mediation model explained 44% of the variance in workplace deviance behaviourscan be predicted bythe fear of COVID-19, perceived organisational support and workplace phobia. The findingsshowthat perceived organizational support reduces the negative impact of COVID-19 fear on workplace phobia and deviance. Results suggest that to reduce the negative effect of the pandemic, organisations should adopt support measures across different managerial levels at different scales rather than providing one-size-fits-all solutions. 2023 The Authors. Stress and Health published by John Wiley & Sons Ltd. -
Achieving organizational performance by integrating industrial Internet of things in the SMEs: a developing country perspective
Purpose: This research investigates the adoption of the industrial Internet of things (IIoT) in SMEs to achieve and increase organizational performance. With the latest technology, small and medium-sized enterprises (SMEs) can create a competitive edge in the market and better serve customers. Design/methodology/approach: Twelve hypotheses are proposed for this study. This study constructed a questionnaire based on technological, organizational, environmental and human perspectives. A survey is conducted on the SMEs of India using the questionnaire. Findings: Eight hypotheses were accepted, and four hypotheses were not supported. The hypotheses rejected are infrastructure, organizational readiness, internal excellence and prior experience. The findings suggested that adopting IIoT in SMEs will increase organizational performance. Research limitations/implications: This study will be helpful for the manager, top management and policymakers. This study identified the areas SMEs need to work on to adopt the technologies. Originality/value: In the literature, no article considered IIoT adoption in SME firms as a human factor. Therefore, this study is unique, including human, technological, organizational and environmental factors. 2023, Emerald Publishing Limited.