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Wireless Sensor Networks in Precision Monitoring of Crops
The sensor-based breadboard is rapidly covering almost every application from human health monitoring to prediction of diseases in accordance with the weather change. This paper presents a sensor based precision crop monitoring system for agriculture application and estimates the energy consumption of the sensor nodes. This high accuracy energy efficient system drastically reduces the damages to the crops and investment made to it. The main focus of the proposed research work is to reduce the energy consumption and minimize the traffic between the nodes of the sensor during the transmission of sensor information. The qualitative metrics has been carried to evaluate the performance of the proposed system which outperform the existing scenario. 2022 IEEE. -
Wood Type Identification via Neural Networks and Spectral Analysis: An Advanced Algorithmic Solution
Forestry management, woodworking, and manufacturing need wood type identification. This study introduces a neural network-spectral analysis technique for accurate and automatic wood type detection. Principal Component Analysis (PCA) is used to extract features from a heterogeneous collection of wood spectral signatures after training a neural network. The algorithm's 94.2% accuracy on a testing dataset shows its ability to distinguish wood kinds.The model's confusion matrix shows it can recognise closely related wood species with few misclassifications. The neural network's precision, recall, and F1 score prove its wood classification accuracy. With PCA highlighting classification characteristics, spectral analysis helps the algorithm succeed.The method is useful for forestry management and woodworking quality control. The non-destructive technology provides in-situ wood type detection, addressing environmental and conservation issues. The study explores ramifications, constraints, and future algorithm modification and application in real-world contexts.Neural networks and spectral analysis provide a strong, efficient, and non-destructive wood type detection solution. The hopeful results represent a major advance in wood science and current computer methods, with applicability across sectors. 2023 IEEE. -
Word-of-Mouth Promotion: How to Attract Consistent Consumers as a Promoter for the B2C Model
The primary goal of this research article is to discover the consumers' behaviour while spending time on the e-commerce platform and to use the consumers who have positive Word-of-Mouth on the products to motivate them as promoters through positive Word of Mouth behaviour. The behavioural factors considered in this study are Relationship value, Trust value, Satisfaction level and Word of Mouth. The trial model includes the consumers who use an e-commerce platform for their online shopping in India. A proper questionnaire with four components was created and used to collect the sample data. Totally 300 responses have been received and analysed with the help of structured equation model and SPSS and AMOS software. The findings suggest that the 'Word of Mouth' technique can be used as a tool to increase the number of consumers in an online platform, particularly e-commerce. We investigated how Relationship Value and Trust Value can be used as key factors to motivate consumers' positive WoM behaviour. This research would be more beneficial to the B2C model. The research has done only for Indian e-commerce portals for survey. There is a scope to do research for the global level e-commerce market. Future study focuses on dynamic attributes for relationship values. This research work will help the researchers who is working on B2C model and consumer behavioural models. This model would be used for any online transactions-based services. As best of the knowledge of the authors' this study is the novel idea to understand the consumers' behaviour for purchasing items through the positive WoM. This work can be adopted for any e-commerce platform. 2024 IEEE. -
Workplace spirituality in the Indian IT sector: development and validation of the scale
The Indian information technology (IT) sector faces a unique challenge of managing their knowledge workers. Workplace spirituality, defined as recognising employees as a spiritual being, is seen as a new solution to the challenges faced by the IT sector. There are many conceptual models, but very few empirical ones to measure spirituality at work in the Indian context. The present study aims to develop an instrument that measures workplace spirituality. In-depth interviews were conducted with 20 IT professionals and seven themes emerged from these interviews, based on which a 66-item questionnaire was developed which was further reduced to 33 items as per recommendations from experts. The questionnaire was administered to 172 Indian IT professionals and its reliability and construct validity were determined using convergent and discriminant validity. As a future scope, the questionnaire could be tested in other sectors and suitable changes be generalised in the Indian context. Copyright 2022 Inderscience Enterprises Ltd. -
X -ray diffraction and microhardness studies of tin monoselenide
Tin Monoselenide (SnSe) crystals have been grown by the Physical Vapour Deposition (PVD) method. X-ray diffraction studies were carried out on the as grown crystals and the lattice parameters were found to be a=11.506 b=4.149and c=4.447 The values were found to be comparable with that reported in the PDF card for SnSe. The microhardness of the crystals has been determined by using Vickers microhardness indenter. 2011 American Institute of Physics. -
X-Tract: Framework for Flexible Extraction of Features in Chest Radiographs for Disease Diagnosis Using Machine Learning
Various types of medical images are used as diagnostic tools for identifying pathologies in human bodies, and in this research, chest X-ray images are used as diagnostic tools. Several pre-built models are created by the participants of ImageNet competitions for non-medical images, and these models are also being used in medical image classification; for example, Khan et al. (Comput Methods Prog Biomed 196:105581, 2020) developed a model called Coronet and Narayan Das et al. (IRBM 1:16, 2020) proposed a deep transfer learning-based model. Instead of using the pre-built models, a different approach was taken to address this problem. A framework was created to extract the frequency and spatial domain-based features, along with the raw statistics of the images. The model proposed in this article using the SVM algorithm has reached accuracy levels ranging from 91% to 97% and sensitivity of 92% to 96% on various samples of test data of over 400 images. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
XGBoost Classification of XAI based LIME and SHAP for Detecting Dementia in Young Adults
As technology progresses on a fast pace, it is imperative that shall be used in the field of medicine for the early detection and diagnostics of dementia. Dementia affects humans by deteriorating the cognitive functions, and as such many algorithms have been used in the detection of the same but all these algorithms remain a black box to the medical fraternity which is still dubious about the nature and credibility of the prediction. To ease this issue, the use of explainable artificial intelligence has been proposed and implemented in this paper, which makes it easy to understand why and how the model is giving a particular output. In this paper the XGBoost classification algorithm has been used which give an accuracy of 93.33% and to understand these predictions, two separate algorithms namely Local Interpretable Model-agnostic Explanations (LIME) and Shapely Additive Explanations (SHAP) have been used. These algorithms are compared based on the type of explanation they provide for the same input and thus the weakness of LIME algorithm has been found out at certain intervals based on the clinically important features of the dataset. On the other hand, both the algorithms make it easy for medical practitioners to understand the dominating factors of a predicted output thereby helping to eliminate the black-box nature of dementia detection. 2023 IEEE. -
Yoga Posture Recognition Using Image Processing
Yoga is an ancient Indian practice that focuses on maintaining balance through various techniques like asanas and meditation. Traditional Indian yoga involves physical postures, regulated breathing, meditation, and relaxation techniques. The practice, rooted in physical, mental, and spiritual disciplines, offers numerous benefits. In this paper, we present an approach for classifying four prominent yoga poses: Goddess Pose (Utkata Konasana), Tree Pose(Vrksasana), Dead Body Pose (Savasana), and Downward Dog Pose (Adho Mukha Svanasana) using image processing techniques. The proposed methodology leverages sophisticated feature extraction techniques that analyse the posture's shape to help capture the details of the posture like the centroid, eccentricity, convex hull, etc. The subsequent classification process employs Support Vector Machines (SVM) enabling accurate categorization based on the extracted features. This blend of traditional wisdom and modern technology offers a promising tool for automating posture recognition, benefiting yoga practitioners and instructors, and can be extended to other real-life scenarios like odd posture detection. 2024 IEEE. -
Zero Trust-Based Adaptive Authentication using Composite Attribute Set
Rapid evolution of internet-oriented applications has increased the threats to confidential data. Single-factor authentication approaches are no longer sufficient to ensure user credibility. Multi-factor authentication schemes are also not tamper-proof. A Zero Trust, adaptive authentication-based approach that uses the user's past behavior can offer protection in this scenario. This paper proposes a system that collects a composite attribute set that includes the user behavior, attributes of the application through which the user is requesting access, and the device used. The enhanced collection allows the creation of detailed context that allows granular variance calculation and risk score. 2021 IEEE. All Rights Reserved. -
Zirconia based pyrochlore thermal barrier coatings
Improvements in thermal barrier coatings (TBCs) technology, further than what is already in service to enable adequate protection to metallic components from higher (>1100C) operating temperatures requires newer developments in materials. Many research activities have been undertaken by scientists to seek alternatives after discovering the threshold of Yttria-stabilized zirconia (YSZ) TBCs on standard aero-space materials at elevated temperatures. To increase the thermal performance of gas turbine engines, alternate TBC materials with better sintering resistance and lower thermal conductivity are required. One of the promising candidates for the TBCs is Pyrochlore-type rare earth zirconium oxides (Re2Zr2O7, Re = rare earth). Re2Zr2O7 TBCs have higher phase stability, lower thermal conductivity, lower sintering rate, no phase transformation, and lower coefficient of thermal expansion at elevated temperatures when compared with YSZ. In this work, plasma spray powders of Lanthanum Zirconate (La2Zr2O7) and Lanthanum Ceria Zirconate (La2 (Zr0.7Ce0.3)2O7) were synthesized by the solid-state reaction method with the goal to develop pyrochlore oxide-based coatings with desired properties at high temperatures (>1200C), better than the YSZ TBCs: Currently the most popular choice for TBCs. These TBCs are expected to increase gas turbine efficiencies while protecting the underlying metallic substrate at high operation temperatures. The evaluation of the synthesised TBCs has been carrying out by studying their performances at 1200C. Results of evaluation for phase composition by employing X-Ray Diffractometry (XRD), microstructure via Scanning electron Microscope (SEM) and chemical composition via Energy Dispersive spectroscopy (EDS) also have been included. Published under licence by IOP Publishing Ltd.