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Impact of Work from Home During COVID-19 Scenario
In view of the recent situation, COVID-19 has spread across the world, and every country has to enforce a lockdown to prevent the virus from transmitting further. The worldwide COVID-19 outbreak has led to a large number of professionals work from their homes. Almost all the sectors like IT, academics, government, business, etc. are implementing work from home for safety of their employees and sincerely obeying the social distancing norms. Work from home can be beneficial and fruitful in terms of travel expenses, saving time commuting, working on ones own agenda, etc. But it can also be a pain and take a toll on mental well-being as you are living a quarantined life with little to no social life, which can also impact an individuals efficiency. There are so many barriers to work from home (WFH), like unavailability of resources, poor network connectivity, using digital platform and latest software for non-IT professionals, lack of proper infrastructure, etc. Our chapter focuses on every aspects of WFH during the COVID-19 lockdown period so that well-suited policies and practices can be designed to cope with the issues and hence transforming future of organizations by shifting the tradition of work from office to work from home. 2024 Apple Academic Press, Inc. All rights reserved. -
Strength and durability properties of geopolymer paver blocks made with fly ash and brick kiln rice husk ash
In India the generation of agro waste rice husk ash is abundant. The utilization of rice husk ash in development of geopolymer binders can be suitable to alleviate the environmental problems associated with disposal of rice husk ash. Further, the utilization of rice husk ash generated from the stacks of brick kilns has not been addressed in past, particularly in development of geopolymer binders. This study proposes development of geopolymer paver (GEOPAV) blocks utilizing brick kiln rice husk ash (BKRHA). It presents fresh, mechanical and durability properties of GEOPAV blocks blended with fly ash, BKRHA, natural aggregates, NaOH and Na2SiO3 solution, and cured in both sundry and room temperature conditions. Microstructural analysis using scanning electron microscope (SEM) and X-ray diffraction (XRD) was adopted to study the influence of BKRHA on hardened properties of GEOPAV blocks. The results show that addition of BKRHA reduce the workability of GEOPAV mixes due to micro porous surface with honeycombed structure of BKRHA particles. The addition of BKRHA showed negligible improvement in compressive strength of GEOPAV blocks. However, the major advantage was observed with improved split tensile strength and flexural strength for GEOPAV blocks with BKRHA. Further, the durability properties in terms of resistance to acid and frost attack was significantly improved with the addition of BKRHA in GEOPAV blocks. Such improvements can be attributed to high amounts of amorphous silica in BKRHA which contribute towards dissolution and formation of polymeric gel, and thereby serve as a binder to enhance the geopolymer matrix making it dense. Finally, all the developed GEOPAV blocks satisfy the IS 156582021 specification requirements and perform much better when compared to commercially available paver blocks. 2021 The Authors -
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. -
Analysis of error rate for various attributes to obtain the optimal decision tree
The competitiveness and computational intelligence are required to increase the gross profit of the product in a market. The classification algorithm rpart is applied on retail market dataset. The regression rpart decision tree algorithm is implemented with principal component analysis to impute data in the missing part of the dataset. The objective is to obtain an optimal tree by analysing cross validation error, standard deviation error, and number of splits and relative error of various attributes. The results of various attributes by ANOVA method are compared to choose the best optimal tree. The tree with minimum error rate is considered for the optimal tree. Copyright 2022 Inderscience Enterprises Ltd. -
Optimized uplink scheduling model through novel feedback architecture for wimax network
Broadband Wireless Access has drawn the fine attention due to the wide range of data requirement and user mobility all the time. Moreover, WiMAX provides the best QoE (Quality of Experience) which is based on the IEEE 802.16 standards; this includes several services such as data, video and audio. However, in order to provide the effective and smooth experience i.e. QoS scheduling plays one of the critical part. In past several mechanism has been proposed for effective scheduling however, through the research it is observed that it can be furthermore improvised hence in this we propose a mechanism named as OUS (Optimized Uplink Scheduling) which helps in improvising the QoS. In here, we have proposed a novel feedback architecture and proposed optimized scheduling which helps in computing the bandwidth request this in terms helps in reducing the delay as well as jitter. Moreover, the performance evaluation is performed through extensive simulation by varying the different SS and frequency and the results analysis confirms that our mechanism performs way better than the existing algorithm. BEIESP. -
Adaptive uplink scheduling model for WiMAX network using evolutionary computing model
The increased usage of smart phones has led to increase usage an internet based application services. These application requires different quality of service (QoS) and bandwidth requirement. WiMAX is an efficient network to provision high bandwidth connectivity and coverage to end user. To meet QoS requirement the exiting model used adaptive model selection scheme. However, these model induce bandwidth wastage as it does not considers any feedback information for scheduling. This work present an Adaptive Uplink Scheduling (AUS) by optimizing MAC layer using Multi-Objective Genetic Algorithm (MOGA). The MAC scheduler use feedback information from both physical layer and application layer. Further, to meet QoS requirement of application and utilize bandwidth efficiently this paper presented an adaptive modulation selection scheme based on user application requirement using MOGA. Our model provides application level based QoS provisioning for WiMAX network. Experiment are conducted to evaluate performance of AUS over exiting model. The overall result attained shows AUS model attain good performance in term of throughput, successful packet transmission and packet collision. 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Evalutionary compting model for QoS provisioning in WiMAX
In recent time wireless technology is adopted widely for connecting remote user over network. WiMAX is an attractive technology for provisioning high data rate connectivity and coverage. QoS is a required parameter for analyzing the system performance. Before allocation of the bandwidth in the network, physical layer information is required for improving QoS performance. Many modulation techniques are used in WiMAX network. An adaptive approach is required for selection of modulation scheme to maximize network performance. Physical layer information is used for selection of modulation scheme. An adaptive genetic based scheduling is proposed in this paper for improved QoS. Experiments are conducted to evaluate performance of proposed approach in term of throughput, successful transmission and packet collision over existing approach. The outcome shows significant performance over existing approach. 2017 IEEE. -
A new facile synthesis of (2S,5S)-5-hydroxypipecolic acid hydrochloride
A simple and efficient synthesis of (2S,5S)-5-Hydroxypipecolic acid hydrochloride is reported. The key features of the synthesis involve the asymmetric reduction of ketone using (S)-CBS oxazaborolidine and the use of commercially available methyl pyroglutamate as a starting material.. 2022 Taylor & Francis Group, LLC. -
Convolutional Neural Networks for Automated Detection and Classification of Plasmodium Species in Thin Blood Smear Images
There has been a continued transmission of malaria throughout the world due to protozoan parasites from the Plasmodium species. As for treatment and control, it is very important to make correct and more efficient diagnostic. In order to observe the efficiency of the proposed approach, This Research built a Convolutional Neural Network (CNN) model for Automated detection and classification on thin blood smear images of Plasmodium species. This model was built on a corpus of 27558 images, included five Plasmodium species. Our CNN model got an overall accuracy of 96% for the cheating detection with an F 1score of 0.94. In the detection of the presence of malaria parasites the test accuracy conducted was as follows: 8%. Species-specific classification accuracies were: P. falciparum (95.7%), P. vivax (94.9%), P. ovale (93.2%), P. malaria (92.8%) and P. Knowles (91, 5%). As for the model SL was found to have sensitivity of 97.3% And the specificity in this case is 9 6. 1 %. The proposed CNN-based approach provides a sound and fully automated solution for malarial parasite detection and species determination, which could lead to better diagnostic performances in day-to-day practices. 2024 IEEE. -
Integration of sustainability in business through finance
[No abstract available] -
Data-Driven Decision Making in the VUCA Context: Harnessing Data for Informed Decisions
Data-driven decision making (DDDM) has evolved from being a strategic advantage to a necessity for organizations aiming to thrive in the dynamic business contexts. It is about using data as a tool to enhance strategic thinking, scenario planning, and adaptation in rapidly changing environments. It involves leveraging data and analytics to navigate the challenges of volatility, uncertainty, complexity, and ambiguity. By embracing DDDM, organizations can enhance their decision-making processes, gain a competitive edge, and navigate the challenges of volatility, uncertainty, complexity, and ambiguity with greater confidence. However, successful implementation requires addressing challenges, fostering a data-driven culture, and continually adapting best practices to meet the evolving demands of the VUCA environment. This chapter discusses how organizations leverage DDDM in VUCA context to support effective and rapid decision making aligned with organizations vision. Particularly, it would offer insights to transit from volatility to vision, uncertainty to understanding, complexity to clarity, and ambiguity to agility. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Stakeholders' pedagogical preferences for teaching 'marketing' in management education
This study has been realized that there is a dire need for re-thinking, particularly obvious for matters of assessment and its relation to the current focus on teaching marketing. A descriptive design of the research was used where convenient sampling has been followed for data collection. In order to achieve the purpose, it was decided to collect independent opinions of students, teachers, and professionals. Analysis has been done through descriptive statistics and Spearman's rank correlation. As a result, a significant difference between the stakeholders' perceptions about the pedagogy for teaching marketing in management education was identified. 2021 Ecological Society of India. All rights reserved. -
Occupational stress: A pre and post COVID-19 perspective on teaching personnel in higher education institutions of India
The COVID-19 as a catalytic phenomenon exposes many loop holes in the socio-economic sustainability of business and society. It exposed people to different occupational stressors and anxiety. This study was focused on occupational stress of teaching personnel in higher education institutions (HEIs), in both pre and post COVID-19 scenario. Descriptive analysis shows work autonomy, career progression, community membership, work conditions, and freedom to use own judgements are major stressors to to HEI teachers in the post COVID-19 scenario. Inferential evaluation has confirmed that Job security, social service, and creativity are major concerns to HEI teachers. They experience limitations to try their own ways of doing job. 2021 Ecological Society of India. All rights reserved. -
Comparative Analysis of Machine Learning Models in Predicting Academic Outcomes: Insights and Implications for Educational Data Analytics
In the evolving landscape of educational research, the predictive analysis of student performance using data science has garnered significant interest. This study investigates the influence of diverse factors on academic outcomes, ranging from personal demographics to socioeconomic conditions, to enhance educational strategies and support mechanisms. We employed a diverse ml models to analyze a information containing academic records and socioeconomic information. The models tested include Logistic Regression, Random Forest (RF), Gradient Boosting (GB), Support Vector Machines (SVC), K-Nearest Neighbors (KNN), Gaussian Naive Bayes, and Decision Trees. The process involved comprehensive data preprocessing, exploratory analysis, model training, and evaluation based on metrics such as precision, recall, accuracy, and F1 score. The results indicate that ensemble methods, specifically RF and GB, demonstrate superior efficacy in accurately predicting categories of student performance such as 'Enrolled,' 'Graduated,' and 'Dropped Out.' These models excelled in handling the complex interplay of varied predictors affecting student success. The results further underline the potential of advanced ensemble ML techniques in significantly outperforming the prediction accuracy in the academic domain, hence facilitating the tailoring of educational interventions to foster improved engagement and better outcomes for students. This has provided a comparative analysis of the methods that guide the future application of predictive analytics in education. 2024 IEEE. -
Towards an Improved Model for Stability Score Prediction: Harnessing Machine Learning in National Stability Forecasting
In our increasingly interconnected world, national stability holds immense significance, impacting global economics, politics, and security. This study leverages machine learning to forecast stability scores, essential for understanding the intricate dynamics of country stability. By evaluating various regression models, our research aims to identify the most effective methods for predicting these scores, thus deepening our insight into the determinants of national stability. The field of machine learning has seen remarkable progress, with regression models ranging from conventional Linear Regression (LR) to more complex algorithms like Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting (GB). Each model has distinct strengths and weaknesses, necessitating a comparative analysis to determine the most suitable model for specific predictive tasks. Our methodology involves a comparative examination of models such as LR, Polynomial Regression (PR), Lasso, Ridge, Elastic Net (ENR), Decision Tree (DT), RF, GB, K-Nearest Neighbors (KNN), and SVR. Performance metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared (R2) assess each model's predictive accuracy using a diverse dataset of country stability indicators. This study's comprehensive model comparison adds novelty to predictive analytics literature. Our findings reveal significant variations in the performance of different regression models, with certain models exhibiting exceptional predictive accuracy, as indicated by high R2 values and low error metrics. Notably, models such as LR, SVR, and Elastic Net demonstrate outstanding performance, suggesting their suitability for stability score prediction. 2024 IEEE. -
Prediction of Stock Prices using Prophet Model with Hyperparameters tuning
As part of the data analytical process, predicting and time - series are crucial. In academics and financial research, anticipating share prices is a prominent and significant subject. A share market would be an uncontrolled environment for anticipating shares since there are no fundamental guidelines for evaluating or anticipating share prices there. As a result, forecasting share prices is a difficult time-series issue. fundamental, technical, time series predictions and analytical strategies are just a few of the various techniques and approaches that machine learning uses to execute stock value predictions. This article implements the stock price prediction, Researchers compared the model of the prophet with the tuned model of the prophet. By utilizing the tuning of hyperparameters using parameter grid search to improve the performance of the model accuracy for the best prediction. The findings of the study demonstrated that tuned model of the prophet with hyperparameters tuning which results in model accuracy and based on the experimental findings mean squared error (MSE) and mean absolute percentage error (MAPE) has significant improvement. 2022 IEEE. -
PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile Applications
Traditional recommendation approaches for the mobile Apps basically depend on the Apps related features. Now a days many users are in quench of Apps recommendation based on the version description. Earlier mobile Apps recommendation system do not handle the cold start problem and also lacks in time for recommending the related and latest version of Apps. To overcome this issues, a hybrid Apps recommendation framework which is considering the version of the mobile Apps is proposed. This novel framework named 'Probabilistic Evolution based Version Recommendation Model (PEVRM)' integrates the principles of Probabilistic Matrix Factorization (PMF) with Version Evolution Progress Model (VEPM). With the help this novel recommendation algorithm, the mobile users easily identify the specific Apps for particular task based on its version progression. At same time, this framework helps in resolving cold start problems of new users. Evaluations of this framework utilize a benchmark dataset, i.e., Apple's iTunes App Store3, for revealing its promising performance. 2013 IEEE. -
Leadership management strategies and organisational practices with respect to the hotel sector of rainbow tourism group limited
This was a research exercise which sought to explore leadership management strategies and organisational practices taking place in the hotel sector of Rainbow Tourism Group Limited (RTGL). RTGL is one of Zimbabwe s biggest hotel and tourism sector. The hotel portfolio are comprised of Rainbow Towers and Conference Centre, Bulawayo Rainbow, Victoria Falls Rainbow, Kadoma Rainbow and Conference Centre, A Zambezi River Lodge and Ambassador Hotel. The six hotels have a combined total of 886 rooms, with the largest number of 304 rooms being in the five star hotel, Rainbow Towers and Conference Centre and while the rest in the three star group hotels. Other operations outside Zimbabwe are Hotel Edinburg, the Savoy Hotel and a hotel in Mozambique. The problem statement of the research study was to examine the role of organisational culture in shaping the leadership strategies in hotel and catering sector, organizational leadership and their effectiveness in helping to achieve organizational objectives. The following was newlinethe set of objectives that the research sought to achieve. Firstly, the research sought to determine and analyze the different types of leadership strategies adapted by the hotel sector of Rainbow Tourism Group Limited in the hotel sector a Case Study. The second newlineobjective was to determine the environment forces affecting the acceptance and newlineassimilation of the mentioned strategies. Thirdly the research sought to ascertain the newlineefficacy of mentioned strategies in attaining these organisational strategies in the hotel newlinesector of RTGL. The fourth objective was to suggest if any alternate strategies will be newlinerequired to enhance leadership effectiveness in Hotel Sector of the RTGL. Finally the newlineresearch sought to develop a Leadership Model that can be used in the hotel industry. -
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INTERFACING PRIMAL RELIGION OF THE HAMAI (ZELIANGRONG), CHRISTIANITY, HERAKA, AND TINGKAO RAGWANG CHAPRIAK
This chapter explores the intertwinement between four religious traditions, namely (1) Characheng (primal religion of the Hamai) and its offshoots, (2) Heraka, (3) Tingkao Ragwang Chapriak (TRC), and (4) Christianity in contemporary Hamai (Zeliangrong) communities. The influence of the primal Hamai religion on Christianity is unquestionable, and at the same time, these two traditions hold sway over Heraka and TRC in varying degrees. The impacts of the interaction are at the levels of consciousness, belief systems, practices, and values. The chapter brings out the asymmetric encounter between reformed religious traditions (Heraka) of the Hamai and the proselytisation of Christianity in the Hamai communities that had led to the extinction of the primal religion of the former. Remarkably, Heraka and TRC are counter-proselytising movements against Christianity based on the primal belief system and synthesis of Christian and Hindu belief systems. For this purpose, the research employs comparative and dialogical approaches to explore and analyse the interconnection among the above religions. It argues that the current forms of Christianity, Heraka, and TRC in Hamai tribes are unique in themselves, and at the same time, they are also cyclically inspired by one another in the process of their encounters. 2025 selection and editorial matter, Maguni Charan Behera; individual chapters, the contributors.