Accuracy improvement in odia zip code recognition technique

Odia is a very popular language in India which is used by more than 45 million people worldwide, especially in the eastern region of India. The proposed recognition schemes for foreign languages such as Roman, Japanese, Chinese and Arabic can’t be applied directly for odia language because of the different structure of odia script. Hence, this report deals with the recognition of odia numerals with taking care of the varying style of handwriting. The main purpose is to apply the recognition scheme for zip code extraction and number plate recognition. Here, two methods “gradient and curvature method” and “box-method approach” are used to calculate the features of the preprocessed scanned image document. Features from both the methods are used to train the artificial neural network by taking a large no of samples from each numeral. Enough testing samples are used and results from both the features are compared. Principal component analysis has been applied to reduce the dimension of the feature vector so as to help further processing. The features from box-method of an unknown numeral are correlated with that of the standard numerals. While using neural networks, the average recognition accuracy using gradient and curvature features and box-method features are found to be 93.2 and 88.1 respectively

Electrodeposition of Ferromagnetic Nanostructures

The fabrication of one-dimensional ferromagnetic nanostructured materials such as nanowires and nanotubes by the electrodeposition technique is discussed. The size, shape and structural properties of nanostructures are analysed by controlling the deposition parameters such as precursors used, deposition potential, pH, etc. The growth of nanostructures and various characterization techniques are studied to support their one-dimensionality. A comparative study of ferromagnetic nanowires and nanotubes is made using angular-dependent ferromagnetic resonance technique

An Efficient and Dynamic Path Reconstruction in Wireless Networks

In Big-scale multi-hop wireless sensor networks (WSNs) for informationgathering is the ability of monitoringper-packet routing paths at the destination is essential in better understanding network dynamics, and better routingprotocols, topology control, energy consumption, duplicate detection, and load balance in WSN deployments. Wefirst devise a basic Routing Topology Recovery (RTR) algorithm with the measurement metric of modularsummation and illustrate how basic RTR algorithm works the path determining process from source to destinationfor data transmission is generally known as routing. In most WSNs routing of incoming data can be determined bythe network layer. The SNs at the source cannot reach the sink node directly in multi-hop networks, henceintermediate SNs need to relay their respective packets. Mainly the routing path of every packet is helpful inunderstanding the network performance.We present SANA secure Ad hoc Network Architecture. Its goal lies inmanaging adaptively preventive, reactive and tolerant security mechanisms to provide essential services even underattacks, intrusions or failures. The results reveal that our approach significantly outperforms other state-of-the-artmethods including MNT, Pathfinder, and CSPR. Furthermore, we validate our method intensively with a real-worldoutdoor WSN deployment running collection tree protocol for environmental data collection

A Bibliometric Perspective Survey of Astronomical Object Tracking System

Advancement in the techniques in the field of Astronomical Object Tracking has been evolved over the years for more accurate results in prediction. Upgradation in Kepler’s algorithm aids in the detection of periodic transits of small planets. The tracking of the celestial bodies by NASA shows the trend followed over the years It has been noted that Machine Learning algorithms and the help of Artificial Intelligence have opted for several techniques allied with motion and positioning of the Celestial bodies and yields more accuracy and robustness. The paper discusses the survey and bibliometric analysis of Astronomical Object Tracking from the Scopus database in analyzing the research by area, influential authors, institutions, countries, and funding agency. The 93 research documents are extracted from the research started in this research area till 6th February 2021 from the database. Bibliometric analysis is the statistical analysis of the research published as articles, conference papers, and reviews, which helps in understanding the impact of publication in the research domain globally. The visualization analysis is done with open-source tools namely GPS Visualizer, Gephi, VOS viewer, and ScienceScape. The visualization aids in a quick and clear understanding of the different perspective as mentioned above in a particular research domain search

Developing banking intelligence in emerging markets: Systematic review and agenda

The current banking industry is heavily dependent on technological artifacts supported by intelligent systems for performance on operational and marketing parameters. However, the attributes for enabling practice between such technological interfaces with managerial adoption are been lagging creating a knowledge gap. To address this, present research surveys the prior work from 1970 to 2020 on intelligent decision support models specific to banking. Subsequently, findings are synthesized on quadrant outcomes; technology; employees, customers, and organizations for service ecosystems. In addition, the managerial perceptions of technology on work are captured through short survey. Finally, scope of advancements like big data, internet of things (IoT), virtual reality (VR) along other untapped conceptual relationships into this framework are discussed

Think Happy Be Happy: Salesperson’s Personal Happiness and Flourishing

Although the role of positive emotions is important in sales, personal happiness remains understudied in the selling context. Grounded in broaden-and-build theory, this study aims to examine the relationships among personal happiness, job involvement, job satisfaction and salesperson flourishing. For salespeople, the new demands of a connected world have largely blurred the boundaries between their personal life and work life. It has allowed emotions from their personal life to spill over into their workplace. Data from 137 salespeople in the retail context in India lend support for the proposed serial mediation model. The authors propose that the influence of personal happiness on a salesperson flourishing is mediated by job involvement and job satisfaction. Results of this study shows that personal happiness has a direct influence on the salesperson’s flourishing and is effective only through the mediating influence of job satisfaction and not of job involvement. This study extends the broaden-and-build theory by proposing that personal happiness may influence flourishing at work. The findings illustrate the need for a renewed focus on salesperson’s personal emotions, especially in todays connected workplace where the boundaries between personal and work life are shrinking

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

Background: Hospitals face a significant problem meeting patients' medical needs during epidemics, especially when the number of patients increases rapidly, as seen during the recent COVID-19 pandemic. This study designs a treatment recommender system (RS) for the efficient management of human capital and resources such as doctors, medicines, and resources in hospitals. We hypothesize that a deep learning framework, when combined with search paradigms in an image framework, can make the RS very efficient. Methodology: This study uses a Convolutional neural network (CNN) model for the feature extraction of the images and discovers the most similar patients. The input queries patients from the hospital database with similar chest X-ray images. It uses a similarity metric for the similarity computation of the images. Results: This methodology recommends the doctors, medicines, and resources associated with similar patients to a COVID-19 patients being admitted to the hospital. The performance of the proposed RS is verified with five different feature extraction CNN models and four similarity measures. The proposed RS with a ResNet-50 CNN feature extraction model and Maxwell-Boltzmann similarity is found to be a proper framework for treatment recommendation with a mean average precision of more than 0.90 for threshold similarities in the range of 0.7 to 0.9 and an average highest cosine similarity of more than 0.95. Conclusions: Overall, an RS with a CNN model and image similarity is proven as an efficient tool for the proper management of resources during the peak period of pandemics and can be adopted in clinical settings

High-frequency characterization of Permalloy nanosized strips using network analyzer ferromagnetic resonance

We report on the dynamic properties of Permalloy nanostrips at gagahertz frequencies. The thickness of the strips is 100 nm, strip width is 300 nm, strip spacing is 1 μm, and length is 0.3–100 μm; aspect ratios are 1:1, 1:2, 1:3, 1:5, 1:10, and 1:333. The dynamic behavior was studied by network analyzer ferromagnetic resonance (FMR) using Permalloy strips on a coplanar waveguide in flip-chip geometry. The FMR mode frequencies (fr) can be controlled by the aspect ratio as well as by the applied magnetic field (H). In longer strips (1:10 and 1:333), the excitation frequencies show a soft mode behavior (Heff = 990 Oe) when the field is along the hard axis. However, along the easy axis (along the strip length), fr increases with applied field. At a field of 3 kOe, fr values are almost independent of aspect ratio along the easy axis except for the 1:1 strip. Along the hard axis, the frequencies are strongly dependent upon the aspect ratio. We also observed that the frequency linewidths of the strips are dependent on the aspect rati

Influencer marketing: When and why gen Z consumers avoid influencers and endorsed brands

Consumer avoidance of brands and influencers is a widespread phenomenon, especially among Generation Z (Gen Z); however, influencer marketing literature lacks clarity about when and why Gen Z engages in such avoidance. Our experimental investigation, across four studies, reveals that Gen Z considers brands' control over influencers to be morally irresponsible and, thus, avoids both. We introduce a novel construct, influencer avoidance, and examine its drivers. Study 1 indicates that perceived brand control engenders avoidance; moderation evidence shows that macro (vs. micro) influencers accentuate (attenuate) the influence of brand control on avoidance. Study 2 shows that Gen Z enjoying a strong versus weak relationship with influencers results in lower (higher) avoidance towards influencers and endorsed brands. Study 3 demonstrates that negative moral emotions mediate the relationship between perceived brand control and avoidance behavior. Study 4 generalizes the findings by analyzing a different influencer and endorsed brand and including a prominent advertisement disclosure. By investigating the drivers and mechanisms of Gen Z's avoidance behavior, our research contributes to research on the theory of moral responsibility, Gen Z's influencer avoidance behavior, and anti-consumption literature. This offers key insights into how to prevent acts of consumer retribution towards influencers and brands