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Perceived stress and fatigue in software developers: Examining the benefits of gratitude
Software development demands creativity and adept problem-solving skills. However, long-term stress and fatigue might impede these skills in software developers. From the perspective of positive psychology, this cross-sectional study investigated the effects of gratitude, age, and gender on stress and fatigue in 421 participants, 244 males (58 %) and 177 females (42 %), aged between 21 and 57 years (M = 36.20; SD = 7.56). The tools employed included the multi-component gratitude measure, perceived stress scale and fatigue assessment scale. Multiple linear regressions confirmed the beneficial effects of gratitude, and they indicated higher levels of perceived stress and fatigue in women and younger professionals. These findings have positive implications for organisational psychologists, as they signify the favourable impacts of gratitude in mitigating stress and fatigue in software developers. The authors recommend that organisational practitioners should focus on enhancing the professionals' well-being by strategizing and implementing gratitude training programmes. 2022 Elsevier Ltd -
Perception of Climate Finance: An Empirical Approach
Climate finance is an alternative financing source in which private and public at domestic and global levels invest their funds to support mitigation of and adapt to present and upcoming climate change. It is an enormous challenge since it is incredibly susceptible to climate impact. The main challenge lies in identifying risks of climate change, appropriate response measures, and prioritizing them to control climate change. The paper aims to determine the perception of climate finance among the public while assessing India's current situation concerning climate change. A well-structured questionnaire was prepared, and data were collected from 253 respondents in Chennai city from December 2020 to February 2021 using a convenience sampling method. A chi-square tool was used to examine the association between the demographic profiles of the respondents and the respondents' perception of climate change-related activities. Type of family, age, and number of family members are significantly associated with most statements connected to the perception of climate finance. The majority of the respondents had insufficient knowledge about climate change policies. Forty-two per cent of the respondents believed that the investment made in climate finance is used effectively for sustainable development. It explores the present scenario of climate finance in India during the Covid 19 pandemic period. The study results will be helpful to the social investment companies, and the regulators frame suitable strategic policies. 2022 by authors, all rights reserved. -
Perception of Entrepreneurial Ecosystem: Testing the ActorObserver Bias
Entrepreneurial ecosystem is the interacting socio-economic environment that facilitates entrepreneurs to start and develop their enterprises. A vibrant and supportive entrepreneurial ecosystem is necessary for the start-up and growth of an enterprise. The entrepreneurial action would largely depend on the perception of entrepreneurs about the ecosystem. In this context, a study was designed to understand the perceptions of actors (entrepreneurs) and observers (non-entrepreneurs) on various components of the entrepreneurial ecosystem. Data for this study were collected from 296 entrepreneurs and 315 non-entrepreneurs from India, who responded to a 77-item questionnaire by giving their ratings of various aspects of the ecosystem on a 5-point scale. Findings of the study showed that perceptions of the entrepreneurial ecosystem were significantly different for most of the subgroups. Most notable among these differences was those between entrepreneurs and non-entrepreneurs, where the mean scores on all dimensions were found to be significantly higher for non-entrepreneurs than for entrepreneurs except for entrepreneurial capability which was found to be higher for entrepreneurs. Hence, the hypothesis of actorobserver bias in the perceptions of entrepreneurs and non-entrepreneurs is supported. 2019 SAGE Publications. -
Perception of information and communication technology tools among small and medium enterprises in Bengaluru
The Small and Medium Enterprises (SMEs) sector is a critically important sector. Despite its large contribution to the economy of the country, SMEs are not in a good position in terms of finance, technology and markets at present. The major problem faced by SME?s in India is the adoption of technology. The basic aim of this study is to evaluate the Information and Communication Tools (ICT) adoption by SME?s in India. For the study, a survey consisting of a self-administered questionnaire was conducted. The study utilized correlation and regression analysis. The findings prove that the institutional pressures have no significant influence on the advantages of ICT adoption, Challenges of ICT adoption and Awareness of different government schemes. Benefits of ICT adoption has moderate influence on Challenges of ICT adoption. The study showcases the factors that motivate entrepreneurs, firm owners to adopt ICT, and the challenges that an SME will face for ICT adoption. 2020, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Perception vs. reality: Analysing the nexus between financial literacy and fintech adoption
Fintech has revolutionized the financial services sector, fundamentally transforming how individuals and businesses manage their finances. However, effective and responsible utilization of these innovative services may require a certain degree of financial competence. To explore this possibility, this study investigates the nexus between financial literacy and fintech usage in the Indian context, considering two distinct measures of financial literacy. Primary data were collected conveniently from 391 respondents through a cross-sectional survey. Probit regression was applied to analyze the relationship between the two dimensions of financial literacy and the adoption of fintech services across three segments: mobile banking, mobile payments, and digital lending. The findings reveal a positive relationship between individuals subjectively perceived financial literacy and their propensity to use all three fintech services. Conversely, objectively measured financial literacy demonstrates a positive association only with the likelihood of using mobile banking. The study also identifies demographic characteristics as contributing factors to variations in fintech adoption. The studys findings hold value for policymakers and fintech service providers, as they underscore the importance of enhancing individuals subjective perceptions of their financial abilities to promote wider adoption of fintech services. Shamli Prabhakaran, Mynavathi L., 2023. -
Perceptions towards Financial Literacy in India
The International Journals Research Journal of Social Science & Management Vol.2, No.9, pp.61-69 ISSUE No. 2251-1571 -
Perceptual gap among corporate world, academics and students: Personal qualities and employability competencies of students
Personal qualities and employability competencies influence how an individual interacts with others. Employers value employability skills because they are linked to how employees get along with co-workers and customers, job performance, and career success of the employee. Hence personal qualities and employability competencies are considered as one of the essential components for an individuals career development. This study aims to understand the perceptual gap among the corporate world, business school academics and business school students. This study is quantitative in nature and primary data was collected through survey method. The primary data was collected from 377 Master of Business Administration (MBA) students, 276 Business School faculties and 98 managers representing 100 different companies in Bangalore, India. Three different questionnaires were prepared for three groups. All three sets of respondents were asked to rate their perception towards the requirement of personal qualities and skill/competencies required at the workplace in an entry-level job. The study highlights that there is a significant difference in the perception of students, business school faculty and managers towards listed personal qualities and competencies. These perceptual differences result in different types of costs to the company in terms of time, money and energy. The results will help the business schools to develop an innovative business curriculum that can fill the current industry needs. 2019, University of Malaya. All rights reserved. -
Perceptual gap among corporate world, academics and students: Personal qualities and employability competencies of students /
Malaysian Online Journal of Educational Management, Vol.8, Issue 1, pp.1-17, ISSN No:2289-4489. -
Perceptual span in reading Aksharic Kannada
Perceptual span, the effective visual field in reading covered in a single fixation, varies across orthographies. The perceptual span for reading English covers 34-character spaces to the left of fixation and around 1415-character spaces to the right of the fixation while for Chinese it is one character space to the left and 3-character spaces to the right of the fixation. In the present study, we estimated the perceptual span for Kannada, a major South Indian language written in akshara (abugida type) using the gaze-contingent moving window paradigm. We recorded eye movements from skilled Kannada readers when they read sentences in different window sizes and compared the eye movement measures with that of full-length sentence reading. Results showed that the perceptual span for Kannada covers one akshara to the left and 6-akshara to the right of the fixation. Future studies need to establish whether all Akshara orthographies show a similar percentual span. 2022, The Author(s), under exclusive licence to Springer Nature B.V. -
Perfectionism and self-compassion among emerging adults: The role of disciplining experiences
Although the influence of disciplining experiences on a variety of personality factors has been studied, there is less clarity on how disciplining experiences influence the traits of perfectionism and self-compassion in individuals. The purpose of this study was to examine the relationships between different domains of perfectionism and self-compassion, as well as the influence of specific aspects of disciplining experiences, such as parental warmth and punishment experiences, on perfectionism and self-compassion. In this study, a quantitative cross-sectional correlational design was used. A total of 220 Indian emerging adults from the city of Bangalore were surveyed via convenience sampling. The following scales were administered: Disciplining Experiences Measure, Multidimensional Perfectionism Scale, and Self-Compassion Scale. The results showed that (1) Self Compassion has a significant positive relationship with Perfectionism; (2) Punishment experience has an influence on Other-oriented and Socially Prescribed Perfectionism; (3) Disciplining Helped positively predicted Self-oriented Perfectionism; and (4) Parental Warmth positively predicted Self-compassion in individuals. The findings contribute to the literature emphasizing the influence of disciplining experiences on ones self and personality, as well as the potential benefits of self-compassion-based interventions. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Performance analysis of alternating minimization based low complexity detection for MIMO communication system
Several antennas are used for sending and receiving in large MIMO (Multiple-Input-Multiple-Output) devices and assist in enhanced performances of wireless communication systems. One important component of Large MIMO systems is that MIMO detectors are placed at receiver ends, whose functions are to regain symbols broadcasts from multiple antennas. In this paper, novelAMLCD (Alternating Minimizationbased Low Complexity Detections) method is proposed in which AMs (Alternating Minimizations) are applied in initial stages to detect signals. Soft value generation is used for the second stage to estimate the signals. Finally, the more optimal estimated signal value will be chosen by applying the MPSOs (Modified Particle Swarm Optimizations). The system's functions are evaluated using CPMs (Continuous Phase Modulations) and channels AWGNs (Additive White Gaussian Noises). According to the results obtained, the suggested AMLCD method with modulations of CPMs outperform known methods using QAMs (Quadrature Amplitude Modulations) under multiple antennas in terms of BERs (Bit Error Rates). The AMLCD method also reduces the time complexity and computational complexity compared to the existing methods. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Performance Analysis of Deterministic Finite Automata and Turing Machine Using JFLAP Tool
In real life, the increased data accessing speed and data storage ability is required by most of the machinery fields. However, the real-world problems can be studied effectively with the combination of scientific computational techniques with the mathematical models. Automata theory is known to be the popular mathematical model. Towards most of the software and hardware related applications, the computational methods are analyzed and designed using significant automata theory concepts (likely, pushdown automata (PDA), Turing machines (TMs) and finite automata (FA)). Hence, the conventional lecture-driven style has attracted the reflective preferences of learners using these abstract natured concepts. But the lecture-driven teaching style has less motivated the computer engineering learners. In order to learn automata theory and computational models, we introduce the PDA and TM in a virtual platform. However, this work has motivated the improvement of longitudinal experimental validation and learning using the modern technology. Java Formal Languages and Automata Package (JFLAP) tool is used to write our simulators in JAVA language and the results are obtained from each machine through simulating the input strings. 2021 World Scientific Publishing Company. -
Performance analysis of different classifier for remote sensing application
The classification of remotely sensed data on thematic map is a challenging task from very long time and it is also a goal of todays remote sensing because of complexity level of earth surface and selection of suitable classification technique. Hence selection of best classification technique in remote sensing will give better result. Classification of remotely sensed data is an important task within the domain of remote sensing and it is outlined as processing technique that uses a systematic approach to group the pixels into different classes. In this study, we have classified the multispectral data of Udupi district, Karnataka, India using different classifier including Support Vector Machine (SVM), Maximum Likelihood, Minimum Distance and Mahalanobis Distance classifier. The data of dimension 3980x3201 pixels are collected from a Landsat-3 satellite. Performance of the each classifier is compared by conducting accuracy assessment test and Kappa analysis. The obtained results shows that SVM will give accuracy of 95.35% and kappa value of 0.9408 respectively when compared other classifier, hence effectiveness of SVM is a good choice for classifying remotely sensed data. BEIESP. -
Performance Analysis of Different Classifiers to Build a Classification Model and to Improve the Vigilance Skills in Crime Detection Using Data Mining Techniques
International Journal of Advanced Research in Computer Science, Vol-3 (7), pp. 314-317. ISSN-0976-5697 -
Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals
Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, Dense-Net201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%. 2022 CRL Publishing. All rights reserved. -
Performance Analysis of Novel Compact Octagonal Shaped Fractal Antenna for Broadband Wireless Applications
Antenna plays an important role in any part of the communication system. It has to be designed very cautiously to provide improved system performance to meet the developments in wireless technologies with various design constraints such as small size, low cost, high data, low power consumption and wideband capabilities. Several efforts have been made by various investigators around the globe to amalgamate benefits of fractal structures with electromagnetic concepts and applications to reduce the size of the antenna without obstructing the performance of the antennas. This paper proposes a novel compact octagonal shaped broadband fractal antenna. The proposed antenna was designed on an inexpensive FR4-epoxy substrate and simulated using the High Frequency Structure Simulator. The antenna resonates in dual bands in 3.8 and 1GHz with lowest return loss of ?32.80dB and gain of 10.22dB while maintaining the VSWR in the 2:1 level. Attempts have been made to reduce the size and improve the bandwidth using fractal concept and truncation of ground plane. The fabricated antenna was verified experimentally and the results are agreeing with the simulations. The point of attraction of this antenna is the use of single patch for broadband coverage with easy fabrication. 2018, Springer Science+Business Media, LLC, part of Springer Nature. -
Performance analysis of semantic veracity enhance (SVE) classifier for fake news detection and demystifying the online user behaviour in social media using sentiment analysis
The increased propagation of fake news is the significant concern in the digital era. Identification of fake news from social media platforms is critical to strengthen public trust and ensure social stability. This research presents an effective and accurate framework for identifying fake news that combines different steps of natural language processing (NLP) technique along with a neural network architecture. A novel semantic veracity enhancement (SVE) classifier is designed and implemented in this work for detecting fake news. The proposed approach leverages the effectiveness of sentiment analysis for identifying misleading or deceptive content and its subsequent implications on the sentiment and behaviour of social media users. A BERT model is used in this research for analysing the sentiments and classifying the texts from the social media platform. By examining the sentiments, the SVE classifier differentiates between real news and fabricated content. To achieve this, three different datasets comprising both actual content and fabricated (tweaked) tweets are employed for training the SVE classifier. The potentiality of the SVE classifier is evaluated and compared with different optimization techniques. The outcome of the experimental analysis shows that the proposed approach exhibits an excellent performance in terms of classifying misinformation from the original information with an outstanding accuracy of 99% compared to other state of art methods. 2024, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature. -
Performance comparison of artificial neural network techniques for foreign exchange rate forecasting
Artificial Neural Networks is one of the promising techniques for forecasting financial time series markets and business. In this paper, Radial Basis Function is used to forecast the daily foreign exchange rate of USD in terms of Indian rupees in India during the period 2009-2014. Here, seven technical indicators like simple moving average of one week, Two week, Momentum, Price rate of change, Disparity 7, Disparity 14, Price oscillator are proposed as inputs for forecasting the time series. In addition, this study compares the four models namely Pattern Recognition Networks, Feed Forward Back Propagation Networks, Feed Forward Networks with no feedback, and Radial Basis Function Network to forecast the daily currency exchange rate during the period. The performance of all these models are analysed from accuracy measures namely Mean Square Error, Mean Absolute Error, Sum Square Error and Root Mean Square Error. From the simulation results, the average performance of Radial Basis Function network was found considerably better than the other networks. Research India Publications. -
Performance evaluation and sustainability analysis of geopolymer concrete developed with ground granulated blast furnace slag and sugarcane bagasse ash
This experimental work aims to determine the workability, strength and sustainability aspects of geopolymer concrete developed with GGBS and SCBA in five different proportions of 100-0%, 95 ? 5%, 90 ? 10%, 85 ? 15%and 80 ? 20%. 8M NaOH concentration and Na2SiO3 solutions are used as an alkaline activator in mixes developed. Na2SiO3 to NaOH ratio of 2.5 and 0.5 alkaline liquid to binder ratio is employed in this study to develop ambient cured geopolymer concrete. The results show that the standard consistency and FST of geopolymer paste increases with an increase in the SCBA content of mixes developed. Cs, Sts and Fs decreased with an increase in the content of SCBA in geopolymer concrete mixes. The 28 days Cs of geopolymer concrete developed under ambient cured condition varied from 63.56 to 39.59MPa. Regression analysis was performed to find the correlation between Sts and Fs to Cs. This study aims to outline a unique technique of utilizing an agro industrial waste by product i.e., sugarcane bagasse ash which in turn reduces disposal problem to some extent. According to the test findings, Sugarcane bagasse ash up to 20% can be used as precursor to develop sustainable geopolymer concrete. Due to the high cost of chemicals and river sand the cost of geopolymer concrete developed is slightly higher than normal concrete. Also, as the percentage of SCBA increase in the geopolymer concrete the demand for energy is reduced. Additionally, incorporation of sugarcane bagasse ash will also reduce disposal problems and reduces CO2 emissions into the atmosphere. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Performance Evaluation Frameworks in the Context of Indian Microfinance Institutions
The paper conducts a detailed examination of the existing evaluative frameworks for microfinance institutions to gauge the differences and similarities. Efficiency evaluates how MFIs are meeting the performance standards considering time and budget constraints. Outreach evaluates the effectiveness of MFIs in reaching the beneficiaries. Relative efficiency scores were calculated using data envelopment analysis and outreach was measured in five different dimensions (pentagon model). Further, cluster analysis assisted in categorizing the MFIs into five value clusters. The study compares both outreach performance and relative efficiency scores employing ANOVA and correlation analysis. The study was conducted among the Indian context when the sector was hit by crisis during 2010. Paper brought out important insights about the sample. Indian MFIs were found to be more socially efficient, since the social dimension taken into consideration was number of female clients and majority of Indian MFIs has exclusive female focus. The correlation tests found that relative efficiency scores are positively related to depth (poor focus) and length (sustainability) outreach. The results showed that cluster analysis model basing outreach scores was more comprehensive and captured more information compared to the data envelopment model relative efficiency scores. The study is original in its approach in using cluster analysis for outreach performance and in the objective of comparing the two different models. 2019 Aruna Balammal et al., published by Sciendo 2019.