The Rayleigh-Bard problem for water with maximum density effects
Linear stability and weakly nonlinear stability analyses are developed for Rayleigh-Bard convection in water near 3.98 C subject to isothermal boundary conditions. The density-temperature relationship (equation of state) is approximated by a cubic polynomial, including linear, quadratic, and cubic terms. The continuity equation, the Navier-Stokes momentum equation, the equation of state, and the energy equation constitute the governing system. Linear stability analysis is used to investigate how the maximum density property of water affects the onset of convective instability and the choice of unstable wave number for four different types of boundary conditions. Then, a weakly nonlinear stability study is done using the spectral Fourier method for isothermal tangential stress-free boundary conditions to quantify the heat transport of the system and demonstrate the transition from regular/periodic convection to chaotic convection. A Stuart-Ginzburg-Landau equation is obtained using the multiscale expansion method. Streamlines and isotherms are presented and analyzed. The influence of maximum density has been shown to delay the onset of instability and is, therefore, a stabilizing mechanism for thermal instability. Due to the maximum density, the onset of chaotic convection is also delayed. Among four different boundaries, the impermeable rigid boundaries require the highest Rayleigh number for instability to begin. Increasing boundary temperatures advance the onset of chaotic convection and improve the heat transport situation. 2023 Author(s).
https://doi.org/10.1063/5.0155687 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165433727&doi=10.1063%2f5.0155687&partnerID=40&md5=f5dfe2e3a559fca6c0aee9f292acc928
Basavarajappa M., School of Mathematical and Statistical Sciences, The University of Texas Rio Grande Valley, Edinburg, 78539, TX, United States, Center of Mathematical Needs, Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Bhatta D., School of Mathematical and Statistical Sciences, The University of Texas Rio Grande Valley, Edinburg, 78539, TX, United States
Study on Falkner-Skan Flow of MWCNT-MgO/ EG Hybrid Nanofluid
In this chapter, a theoretical study of the Falkner-Skan flow and heat transport of a Newtonian ethylene glycol containing MWCNT-MgO hybrid nanoparticles on a wedge-shaped surface using the modified Buongiorno nanofluidic model (MBNM) is performed. The mechanisms of Brownian motion (BM) and microscopic thermophoresis (MT) of solid nanoparticles are implicitly included together with the thermophysical properties. The effects of thermal radiation, the Lorentz force, and Joule heating are examined. The passive control of the nanoparticles and the thermal jump boundary conditions are considered. The governing equations are modeled using the conservation of mass, the Navier-Stokes equation, the conservation of energy, and the conservation of nanoparticle volume fraction. The Prandtl boundary layer and Rosseland heat flux approximations were used. The velocity, temperature, and volume fraction of nanoparticles behaviors are analyzed for various parameters. It is determined that the temperature of the hybrid nanofluid increased due to the presence of Joule heating, radiative heat flux, Brownian motion, and thermophoresis aspects in the system. Furthermore, a hybrid nanoliquid exhibits a higher heat transfer rate than mono nanoliquid and base fluid. 2024 selection and editorial matter, Katta Ramesh, Fateh Mebarek-Oudina, and Basma Souayeh; individual chapters, the contributors.
Mathematical Modelling of Fluid Dynamics and Nanofluids, pp. 393-413.
Basavarajappa M., School of Mathematical and Statistical Sciences, The University of Texas Rio Grande Valley, Edinburg, 78539, TX, United States, Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Bhatta D., School of Mathematical and Statistical Sciences, The University of Texas Rio Grande Valley, Edinburg, 78539, TX, United States
Heat transfer of nanomaterial over an infinite disk with marangoni convection: A modified fouriers heat flux model for solar thermal system applications
Disk; Marangoni convection; Modified Fourier heat flux law; Nanofluid; Solar thermal exchangers; Thermal energy modulations
The demand for energy due to the population boom, together with the harmful consequences of fossil fuels, makes it essential to explore renewable thermal energy. Solar Thermal Systems (STSs) are important alternatives to conventional fossil fuels, owing to their ability to convert solar thermal energy into heat and electricity. However, improving the efficiency of solar thermal systems is the biggest challenge for researchers. Nanomaterial is an effective technique for improving the efficiency of STSs by using nanomaterials as working fluids. Therefore, the present theoretical study aims to explore the thermal energy characteristics of the flow of nanomaterials generated by the surface gradient (Marangoni convection) on a disk surface subjected to two different thermal energy modulations. Instead of the conventional Fourier heat flux law to examine heat transfer characteristics, the CattaneoChristov heat flux (Fouriers heat flux model) law is accounted for. The inhomogeneous nanomaterial model is used in mathematical modeling. The exponential form of thermal energy modulations is incorporated. The finite?difference technique along with Richardson extrapolation is used to treat the governing problem. The effects of the key parameters on flow distributions were analyzed in detail. Numerical calculations were performed to obtain correlations giving the reduced Nusselt number and the reduced Sherwood number in terms of relevant key parameters. The heat transfer rate of solar collectors increases due to the Marangoni convection. The thermophoresis phenomenon and chaotic movement of nanoparticles in a working fluid of solar collectors enhance the temperature distribution of the system. Furthermore, the thermal field is enhanced due to the thermal energy modulations. The results find applications in solar thermal exchanger manufacturing processes. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Basavarajappa M.; Lorenzini G.; Narasimhamurthy S.; Albakri A.; Muhammad T.
Applied Sciences (Switzerland), Vol-11, No. 24
https://doi.org/10.3390/app112411609 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120805767&doi=10.3390%2fapp112411609&partnerID=40&md5=c4602789dd70a8fab689feb50c9467a9
Basavarajappa M., Center for Mathematical Needs, Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, India; Lorenzini G., Department of Engineering and Architecture, University of Parma, Parco Area Delle Scienze 181/A, Parma, 43124, Italy; Narasimhamurthy S., Department of Mathematics, MS Ramaiah Institute of Technology, Bengaluru, 560054, India; Albakri A., Department of Computer Science, College of Computer Science & Information Technology, Jazan University, Jazan, 45142, Saudi Arabia; Muhammad T., Department of Mathematics, College of Sciences, King Khalid University, Abha, 61413, Saudi Arabia
DarcyForchheimer Nanoliquid Flow and Radiative Heat Transport over Convectively Heated Surface with Chemical Reaction
Abstract: Improving the heat transport of energy transmission fluids is a vital challenge in numerous engineering applications such as photovoltaic thermal management, heat exchangers, transport and energy-saving processes, solar collectors, automotive refrigeration, electronic equipment refrigeration, and engine applications. Nanofluids address the challenges of thermal management in engineering applications. The DarcyForchheimer flow of magneto-nanofluid initiated by a stretched plate is investigated with application of the Buongiorno model. The features of the nth order chemical reaction, Rosseland thermal energy radiation, and non-uniform heat sink/source are also scrutinized. The Buongiorno nanoliquid model is implemented, which includes the frenzied motion of the nanoparticles and the thermal diffusion of the nanoparticles (NPs). Thermal and solutal convection heating boundary conditions are also incorporated. Boundary layer approximations are used in the mathematical derivation. The non-linear control problem is deciphered with application of the RungeKutta shooting method (RKSM). The results for the relevant parameters are analyzed in dimensionless profiles. In addition, the friction factor on the plate, the heat transport rate, and the mass transport rate of the nanoparticles are calculated and analyzed. 2022, Pleiades Publishing, Ltd.
Basavarajappa M.; Muhammad T.; Lorenzini G.; Swain K.
Journal of Engineering Thermophysics, Vol-31, No. 2, pp. 261-273.
Basavarajappa M., Center for Mathematical Needs, Department of Mathematics, Christ University, Bangalore, India; Muhammad T., Department of Mathematics, College of Sciences, King Khalid University, Abha, Saudi Arabia; Lorenzini G., University of Parma, Department of Engineering and Architecture, Parma, Italy; Swain K., Department of Mathematics, Gandhi Institute for Technology, Bhubaneswar, India
Study of multilayer flow of a bi-viscous Bingham fluid sandwiched between hybrid nanofluid in a vertical slab with nonlinear Boussinesq approximation
Bi-viscosity Bingham plastic fluids are used to understand the rheological characteristics of pigment-oil suspensions, polymeric gels, emulsions, heavy oil, etc. In many industrial and engineering problems involving high-temperature situation, a linear density-temperature variation is inadequate to describe the convective heat transport. Therefore, the characteristics of the nonlinear convective flow of a bi-viscous Bingham fluid (BVBF) through three layers in a vertical slab are studied. The two outer layers of the oil-based hybrid nanofluid and the intermediate layer of BVBF are considered. The thermal buoyancy force is governed by the nonlinear Boussinesq approximation. Continuity of heat flux, velocity, shear stress, and temperature are imposed on the interfaces. The governing equations are derived from the Navier-Stokes equation, conservation of energy, and conservation of mass for three layers. The nonlinear multi-point (four-point) boundary value problem is solved using the differential transform method (DTM). Converging DTM solutions are obtained, and they are validated. The entropy equation and Bejan number were also derived and analyzed. It is established that the nonlinear density-temperature variation leads to a significant improvement in the magnitude of the velocity and temperature profiles due to the increased buoyancy force, and as a result, the drag force on the walls gets reduced. The drag force on the slab gets reduced by decreasing the volume fraction of nanoparticles. Furthermore, nonlinear convection and mixed convection give rise to an advanced rate of heat transport on the walls and thereby to an enhanced heat transport situation. 2022 Author(s).
Basavarajappa M.; Myson S.; Vajravelu K.
https://doi.org/10.1063/5.0123131 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144389172&doi=10.1063%2f5.0123131&partnerID=40&md5=21c3921081bc9b69521decc293607a21
Basavarajappa M., School of Mathematical and Statistical Sciences, The University of Texas Rio Grande Valley, Edinburg, 78531, TX, United States; Myson S., Center for Mathematical Needs, Department of Mathematics, Christ (Deemed to Be University), Karnataka, Bengaluru, 560029, India; Vajravelu K., Department of Mathematics, University of Central Florida, Orlando, 32816, FL, United States
Significance of exponential space- and thermal-dependent heat source effects on nanofluid flow due to radially elongated disk with Coriolis and Lorentz forces /
Journal of Thermal Analysis And Calorimetry, Vol.141, Issue 3, pp.37-44, ISSN No: 1588-2926.
Basavarajappa Mahanthesh, Giulio Lorenzini, Feteh Mebarek Oudina and Isac Lare Animasaun
Time-dependent flow due to noncoaxial rotation of an infinite vertical surface subjected to an exponential space-dependent heat source: An exact analysis /
Heat Transfer Asian Research, Vol.48, Issue 7, pp.3162-3185
Basavarajappa Mahanthesh, Theres Stany Ashlin, Bijjanal Jayanna Gireesha, Sabir A Shehzad and Muhammad Nauman Bashir
https://onlinelibrary.wiley.com/doi/abs/10.1002/htj.21536
IEEHR: Improved Energy Efficient Honeycomb Based Routing in MANET for Improving Network Performance and Longevity
Energy Efficiency; Honeycomb; Location-Aided Routing; MANETs; Optimal Routing
In present scenario, efficient energy conservation has been the greatest focus in Mobile Adhoc Networks (MANETs). Typically, the energy consumption rate of dense networks is to be reduced by proper topological management. Honeycomb based model is an efficient parallel computing technique, which can manage the topological structures in a promising manner. Moreover, discovering optimal routes in MANET is the most significant task, to be considered with energy efficiency. With that motive, this paper presents a model called Improved Energy Efficient Honeycomb based Routing (IEEHR) in MANET. The model combines the Honeycomb based area coverage with Location-Aided Routing (LAR), thereby reducing the broadcasting range during the process of path finding. In addition to optimal routing, energy has to be effectively utilized in MANET, since the mobile nodes have energy constraints. When the energy is effectively consumed in a network, the network performance and the network longevity will be increased in respective manner. Here, more amount of energy is preserved during the sleeping state of the mobile nodes, which are further consumed during the process of optimal routing. The designed model has been implemented and analyzed with NS-2 Network Simulator based on the performance factors such as Energy Efficiency, Transmission Delay, Packet Delivery Ratio and Network Lifetime. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Baseera A.; Kondaveeti H.K.; Gopikrishnan S.; Bejoy B.J.; Ravichandran C.G.; Santhosh R.; Dhanapal R.
Wireless Personal Communications, Vol-129, No. 3, pp. 1753-1769.
Baseera A., VIT Bhopal University, Bhopal-Indore Highway 18, Madhya Pradesh, Kothrikalan, Sehore, 466114, India; Kondaveeti H.K., School of Computer Science Engineering, VIT-AP University Beside AP Secretariat, Andhra Pradesh, Near Vijayawada, India; Gopikrishnan S., School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, Amaravati, India; Bejoy B.J., Department of Computer Science and Engineering, Faculty of Engineering, CHRIST (Deemed to be University), Kengeri Campus, Mysore Road, Karnataka, Kanmanike, Kumbalgodu, Bangalore, 560074, India; Ravichandran C.G., Department of CSE, Vivekanandha College of Technology for Women, Tiruchengode, India; Santhosh R., Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, Tamil Nadu, Coimbatore, 641021, India; Dhanapal R., Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, Tamil Nadu, Coimbatore, 641021, India
Classification of countries based on development indices by using K-means and grey relational analysis
Development; Grey relational analysis; K-means clustering; Principal component analysis
Clustering countries based on their development profile is important, as it helps in the efficient allocation and use of resources for institutions like the World Bank, IMF and many others. However, measuring the status of development in each country is challenging, as development encompasses several facets such as economic, social, environmental and institutional aspects. These dimensions should be captured and aggregated appropriately before attempting to classify countries based on development. In this context, this paper attempts to measure various dimensions of development through four indices namely, Economic Index (EI), Social Index (SI), Sustainability Index (SUI) and Institutional Index (II) for the period between 1996 through 2015 for 102 countries. And then we categorize the countries based on these development indices using the grey relational analysis and K-means clustering method. Our study classifies countries into four clusters with twelve countries in the first cluster, fifty in second, twenty-seven and thirteen countries in third and fourth clusters respectively. Having taken each of the dimensions of development independently, our results show that no cluster has performed poorly in all four aspects. 2021, The Author(s), under exclusive licence to Springer Nature B.V.
Springer Science and Business Media Deutschland GmbH
Basel S., Department of Economics, Christ University, Karnataka, Bengaluru, 560029, India; Gopakumar K.U., Department of Economics, Sri Sathya Sai Institute of Higher Learning, Andhra Pradesh, Prasanthi Nilayam, 515134, India; Rao R.P., Department of Economics, Sri Sathya Sai Institute of Higher Learning, Andhra Pradesh, Prasanthi Nilayam, 515134, India
Analysis of club convergence for economies: identification and testing using development indices
Club convergence; Globalization; Log t regression; Ordered logit; Transitional behavior
This paper attempts to identify club convergence using the procedure suggested by Phillips and Sul (Phillips and Sul, Econometrica 75:17711855, 2007, Phillips and Sul, J Appl Economet 24:11531185, 2009) based on GDP per capita for 102 countries across the globe for the time period 1996 through 2015. The results indicate the presence of five clubs with four countries belonging to the non- convergent group. After identifying the clubs, the study analyzed the transitional behaviors among the clubs. Finally, to understand the determinant of the club membership, we used the ordered logit model by considering the initial level of GDP, gross capital formation, growth rate of population, and four indices, namely social, governance, sustainability, and globalization as the explanatory variables. The results suggest that the initial level of GDP per capita, gross capital formation, social, governance, sustainability, and globalization are the major factors for determining the club. 2021, The Japan Section of the Regional Science Association International.
Asia-Pacific Journal of Regional Science, Vol-5, No. 3, pp. 885-908.
Springer Science and Business Media B.V.
Basel S., Department of Economics, CHRIST (Deemed To Be University), Hosur Road, Bengaluru, 560029, Karnataka, India; Rao R.P., Department of Economics, Sri Sathya Sai Institute of Higher Learning, Prasanthi Nilayam, Puttaparthi, 515134, Andhra Pradesh, India; Gopakumar K.U., Department of Economics, Sri Sathya Sai Institute of Higher Learning, Prasanthi Nilayam, Puttaparthi, 515134, Andhra Pradesh, India
Synthesis and characterization of alkali-activated binders with slag and waste printed circuit board
Alkali-activated; Characterization; Printed circuit board; Slag; Waste
The global production of printed circuit board (PCB) is expected to rise substantially in the next decade due to the advancement in technology. The production of PCB results in generation of hazardous waste of various kinds, and one such waste is the very fine particles of the board material that is generated due to drilling and other preparatory operations. The disposal of such waste in the environment can result in serious consequences which needs attention. Therefore, recycling of waste printed circuit board (WPCB) can mitigate its harmful effects on the environment and also reduce the remediation costs. In this study, the WPCB is used as a substitute to ground granulated blast furnace slag (GGBFS) in development of alkali-activated binder. Alkali-activated binder was synthesized with GGBFS, WPCB, sodium hydroxide sol. (NaOH), and sodium silicate sol. (Na2SiO3). GGBFS was replaced with WPCB at replacement rates of 0%, 10%, 20%, and 30% by volume. Additionally, the effect of varying concentration of NaOH and Na2SiO3 on the physical and mechanical performance of the binder was studied. The developed binders were evaluated for workability, strength, water absorption, and efflorescence properties. Further, to ascertain its safety on the environment, the toxicity characteristic leaching procedure (TCLP) test was also performed. The results indicate that WPCB characteristics are compatible with GGBFS in terms of its particle size distribution. Moreover, the replacement of GGBFS with up to 20% WPCB provides desirable properties for the alkali-activated binder. However, higher replacements are not recommended, since it had detrimental effect on the mechanical performance of the binder. The study revealed that desirable performance can be achieved for binders with 8 M NaOH and with Na2SiO3 to NaOH ratio of 2, and up to 20% GGBFS replaced with WPCB. The results of TCLP test disclose that the contaminant in the leachate from alkali-activated binders with WPCB are within regulatory limits, and do not pose any threat to the environment. Finally, the outcome of this study provides an innovative approach towards formulation of eco-friendly binder for various construction applications such as foundations, buildings, bridges, pavements, etc. 2024 The Author(s)
Journal of Hazardous Materials Advances, Vol-15
https://doi.org/10.1016/j.hazadv.2024.100455 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202503343&doi=10.1016%2fj.hazadv.2024.100455&partnerID=40&md5=425b9048f4b51d94791b0236620e07fa
Basha G., Department of Civil Engineering, School of Engineering and Technology, Christ University, Bangalore, 560074, India; Hossiney N., Department of Civil Engineering, School of Engineering and Technology, Christ University, Bangalore, 560074, India
Leveraging and Deployment of AI / ML to Simplify Business Operations among Diverse Sectors during Covid-19 Battle
Adoption; Artificial Intelligence; Business Operations; COVID-19; Machine Learning
During the evolution of the COVID-19 outbreak, the necessity for companies to re-evaluate and restructure themselves is still not greater. It will make sense for things to change in the business operations. Most companies redesigned current existing ways of running business operations and capacity to make choices to benefit. The present condition sees Artificial Intelligence as a significant facilitator for companies to make their existing situation better (recover from their economic crisis), reconsider (prepare for a long-term change) and reinvent (completely re-engineer) their business model for long-term gain. Automated bots that could identify items and carry out duties that were previously reserved for people would make companies and other infrastructures operational around the clock, through more significant numbers, and at a lower cost. Simulated actual working conditions, including labour forces, would be created by using Artificial intelligence platforms. Businesses would use machine learning and sophisticated business intelligence to use artificial intelligence to explore better market dynamics and provide consumers with "hyper-personalized" goods. Some of the most compelling case studies can have human intelligence and expertise mixed with AI. Many firms should revamp current business processes and capacity to benefit the company in the near future. In this research paper, we have showcased how artificial intelligence would benefit businesses as they adopt with these current developments and during a condition of pandemic without inhibiting their activities. The research is carried in a descriptive way, choosing the diverse sectors in the economy like Banking & Finance, Manufacturing, Education, Retail, Telecommunications, Entertainment and media to make the research more robust and reliable. 2022 American Institute of Physics Inc.. All rights reserved.
Basha M.D.S.A.; Raju R.P.K.; Sucharitha M.M.; SaravanaKrishnan V.
https://doi.org/10.1063/5.0114082 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144058564&doi=10.1063%2f5.0114082&partnerID=40&md5=aa2892f713d248a904f0e9efd9090b94
Basha M.D.S.A., GITAM School of Business, Gandhi Institute of Technology, Management GITAM University, Karnataka, Bengaluru, India; Raju R.P.K., GITAM School of Business, Gandhi Institute of Technology, Management GITAM University, Karnataka, Bengaluru, India; Sucharitha M.M., School of Commerce, Finance and Accountancy, CHRIST University, Karnataka, Bengaluru, India; SaravanaKrishnan V., School of Commerce, Finance and Accountancy, CHRIST University, Karnataka, Bengaluru, India
Talent retention, job involvement satisfaction, and commitment towards the organization in the IT sector
commitment and Involvement; Job Involvement; Retention
Even if there is presently much need for improvement, the information technology (IT) sector plays a key role in the nation's financial development. With enormous growth potential, India's IT sector is up against fierce competition. Numerous participants are competing with one another for resources and jobs inside the company. The direction of events and the manageability of the IT industry depend on capable employees and their responsibilities and participation. Additionally, there is a grouping of the representatives who possess the capacity. Between duty and association and ability maintenance, work fulfilment plays a crucial guiding role. The goal of the current study is to comprehend the effects of talent retention, job satisfaction, and organizational commitment in the IT industry. In this research, we looked at the variables factor analysis. In Bangalore, we chose to survey workers in the IT industry. To understand the results of Talent Retention, Job Involvement, and Commitment for IT Sector Employees, we collected the data using a questionnaire (Likert-scale), which we then analyzed using spss26. 2023 Author(s).
Basha M.D.S.A.; Sucharitha M.M.; Lakshmi M.B.; Kumar J.P.S.
AIP Conference Proceedings, Vol-2888, No. 1
https://doi.org/10.1063/5.0164526 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176780409&doi=10.1063%2f5.0164526&partnerID=40&md5=f8ece5bf6c983cb3ea8ec862ac0fca3d
Basha M.D.S.A., GITAM School of Business, Gandhi Institute of Technology and Management, GITAM University, Karnataka, Bengaluru, India; Sucharitha M.M., School of Commerce, Finance and Accountancy, CHRIST University, Karnataka, Bengaluru, India; Lakshmi M.B., School of Commerce, Finance and Accountancy, CHRIST University, Karnataka, Bengaluru, India; Kumar J.P.S., GITAM School of Business, Gandhi Institute of Technology and Management, GITAM University, Karnataka, Bengaluru, India
Forecasting the Academic Horizon: Machine Learning Models Unraveling the Complex Web of Student Well-being Determinants
Academic Attributes; Economic Indicators; Graduate; Machine Learning; Student Well Being
In the contemporary academic landscape, the well-being of students is pivotal not only for their individual success but also for the broader educational ecosystem. This study meticulously delves into a rich dataset encompassing diverse student attributes, academic performance metrics, and economic indicators to discern patterns and predictors affecting student well-being. Leveraging a multi-faceted research methodology, we employed various machine learning models, ranging from logistic regression to advanced ensemble methods, aiming to forecast and comprehend the intricate determinants of student outcomes. The research design, underpinned by rigorous exploratory data analysis, revealed intriguing correlations between economic conditions, academic achievements, and students' well-being. The Gradient Boosting model, in particular, showed a significant improvement post hyperparameter tuning, with an accuracy reaching up to 77.63%. On the other hand, models like the Random Forest achieved a base accuracy of 77.29%. These insights highlight the potential of data-driven methodologies in understanding and predicting student well-being. As we stride into an era where data-driven decisions in education are paramount, our findings offer a robust foundation for future endeavors in this realm. Future directions of this study encompass refining prediction models with more granular data, exploring the psychological facets of student well-being, and devising actionable interventions based on the identified predictors. 2023 IEEE.
Basha M.S.A.; Biswas P.; Christina S.; Krishnan D.R.; Martha Sucharitha M.
Proceedings of NKCon 2023 - 2nd IEEE North Karnataka Subsection Flagship International Conference
Institute of Electrical and Electronics Engineers Inc.
Basha M.S.A., Gandhi Institute of Technology and Management (Deemed to Be University), Gitam School of Business, Bengaluru, India; Biswas P., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Christina S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Krishnan D.R., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Martha Sucharitha M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India
A Study on Student Cyber Safety Consciousness in the Light of Online Learning
Cyber Crime; Cyber Security; Information security; Privacy; Students
Our world online and networked is immersed under a wave of populism; populism spreads on the wings of internet. The recent technological advancements like the use of social media platforms and different applications made the information exchange faster and more efficient making the information access easier. To keep our information, gadgets such as cell phones, laptops, desktops, and tablets and also the internet safe, knowledge of cybersecurity is vital everywhere. In many colleges and Universities who are in to interconnected complex systems, data privacy is a huge challenge among their users. In most of the situations, due to lack of knowledge and awareness, users may engage in data breaches knowingly or unknowingly and the complete interconnected systems among the users may have a consequence of a cybercrime. This article seeks to unpack the rise of cyber-crimes and its relationship to cyber security among student groups during the pandemic where much of their interaction is online. The research aims to inquire in to the level of knowledge and awareness on cybersecurity among students during their online learning interaction using a well-structured questionnaire. The questionnaire will be focused on five parts: Awareness and Knowledge, Monitoring and Privilege, Security and Prevention, Protection from malware s and usage of removable Devices. The study is conducted using quantitative research methodology to quantitatively evaluate the knowledge of cybersecurity and inculcate an awareness against Cybercrime protection among the students. Finally, based on the analysis of collected data we present recommendations which will not forego the safety concerns for e mails, viruses, phishing, pop-up windows and forged ads which is a common problem. Some technological solutions and paths for the regulation of the cybercrimes are suggested to the respondents at the end. 2022 IEEE.
Basha M.S.A.; Christina S.; Devi A.U.; Sucharitha M.M.; Maheshwari A.
INDICON 2022 - 2022 IEEE 19th India Council International Conference
https://doi.org/10.1109/INDICON56171.2022.10039968 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149233100&doi=10.1109%2fINDICON56171.2022.10039968&partnerID=40&md5=921bd884740d80ad4b02ce2e1e3dd43b
Basha M.S.A., Gandhi Institute of Technology and Management Deemed to Be University, GITAM School of Business, Bengaluru, India; Christina S., Department of Professional Studies School of Commerce, Finance and Accountancy Christ Deemed to Be University, Bengaluru, India; Devi A.U., SRM Valliammai Engineering College, Department of Management Studies, Chengalpattu, India; Sucharitha M.M., Department of Professional Studies School of Commerce, Finance and Accountancy Christ Deemed to Be University, Bengaluru, India; Maheshwari A., Department of Commerce, School of Commerce, Finance Christ Deemed to Be University, Ghaziabad, India
An Efficient Machine Learning Approach: Analysis of Supervised Machine Learning Methods to Forecast the Diamond Price
Diamonds; Estimated values; Machine learning; Regression Models and Cross validation
Diamond, a found natural process compound of carbon, is one of the hardest and most immensely expensive material known to men, especially more to women. Investments in expensive gems like diamonds are in significant demand. The rate of a diamond, nevertheless, is not as easily calculated as the value of either gold or platinum since so many factors must be taken into account. Because there is such a broad range of diamond dimensions and qualities; as a result, being able to make reliable price predictions is crucial for the diamond industry. Although, making accurate predictions is challenging. In this study, we implemented multiple machine learning techniques employed to the challenge of diamond price forecasting's such as Linear Regression, Random Forest, Decision Tree Random Forest, Cat-Boost Regressor and XGB Regressor. This article's goal is to develop an accurate model for estimating diamond prices based on its characteristics such as weighting factor, cut grade, and dimensions. We compared the sum of estimated values and test values of predicted values with overestimated, underestimated and exact estimations. We applied cross-validation to calculate how much the model deviates from the actual when faced with a difference between the training set and the test set. We predicted values side by side. We performed a comparative analysis of supervised machine learning models with other models to evaluate the model accuracy and performance metrics. The Study's experimental findings show that out of all the supervised machine learning models, Random Forest performs well with R2score and Low RMSE and MAE values and CV Score. 2023 IEEE.
Basha M.S.A.; Oveis P.M.; Prabavathi C.; Lakshmi M.B.; Sucharitha M.M.
2023 International Conference for Advancement in Technology, ICONAT 2023
https://doi.org/10.1109/ICONAT57137.2023.10080618 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153282231&doi=10.1109%2fICONAT57137.2023.10080618&partnerID=40&md5=1b4cedfa4958cc1932e36421c929fccb
Basha M.S.A., GITAM School of Business, Gandhi Institute of Technology and Management, Deemed to Be University, Bengaluru, India; Oveis P.M., GITAM School of Business, Gandhi Institute of Technology and Management, Deemed to Be University, Bengaluru, India; Prabavathi C., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Lakshmi M.B., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India
Gems of Prediction: From Clarity to Carats - Unveiling Diamond Prices with Machine Learning in Waikato Environment for Knowledge Analysis
Bagging; Data Mining; M5P; REP Tree; Weka; Weka Environment
Background: This research focuses on using Weka's toolkit to test machine learning models for predicting diamond prices. The complexity of diamond value characteristics, such as carat, cut, color, and clarity, motivates the study to find the most accurate models. The goal is to promote fairer market processes and customer education. Methods used: The research rigorously preprocesses a diamond attributes dataset using Weka for analysis. Various machine learning algorithms are examined, including simple algorithms like Decision Stump and ZeroR, sophisticated models like M5P and REP Tree, and advanced ensemble approaches like Bagging with REP Tree. Model performance is evaluated using train/test splits (80-70-60%) and cross-validation (5-fold and 10-fold) with metrics such as Correlation Coefficient, MAE, and RMSE. Results achieved: The research finds that ensemble approaches, particularly Bagging with REP Tree, outperform simple and sophisticated models in diamond price prediction. These techniques demonstrate higher accuracy and lower error rates, highlighting the need for multiple models to capture the complexity of diamond valuation. Simple models provide benchmarks and insights into dataset trends but are less precise. Concluding remarks: This study contributes to the understanding of machine learning algorithms for diamond price prediction, an important economic valuation subject. It demonstrates the effectiveness of complex data analysis methods using Weka. The research also highlights the accessibility and sophistication of machine learning at the crossroads, with Weka's cutting-edge algorithms making complicated analytical methods more accessible for practical, everyday use. This work adds to the knowledge of the dynamics of diamond prices and the role of machine learning in economic research. 2024 IEEE.
Basha M.S.A.; Oveis P.M.; Raju R.P.K.; Sucharitha M.M.
International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024
https://doi.org/10.1109/ICDCOT61034.2024.10515908 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193246649&doi=10.1109%2fICDCOT61034.2024.10515908&partnerID=40&md5=ec6f6ef3bebd2cdb6fd0ef8a2402c424
Basha M.S.A., Gitam School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India; Oveis P.M., Gitam School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India; Raju R.P.K., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India
Predicting Graduate Admissions using Ensemble Machine Learning Techniques: A Comparative Study of Classifiers and Regressors
Accuracy Scores; Classification report; Classifier; Machine Learning; Regressors
The goal of this research is to apply machine learning techniques to forecast a student's probability of being accepted into a graduate program. Applicants' GRE and TOEFL grades, university rankings, letters of recommendation, statements of purpose, cumulative grade point averages, and prior research experience are all included in the dataset utilized for this analysis. The goal is to calculate an applicant's expected acceptance rate. This study uses a combination of Classifiers and regressors. Different prediction models are contrasted in this study: Random Forest Classifier (RFC), Decision Tree Classifier (DTC), K-Neighbors Classifier (KNC), Support Vector Classifier (SVC), Gradient Boosting Classifier (GBC), Logistic regression (LR), Support vector Regressor (SVR), Random Forest Regressor(RFR), Gradient Boosting Regressor(GBR) and Decision Tree Regressor(DTR). Using these characteristics, the models are trained and evaluated. Evaluation criteria such as accuracy, kappa value, AUC-ROC, and confusion matrix are used to find the models' effectiveness. In order to determine which model performed the best, the assessment results are compared with one another. Based on study findings, the Gradient Boosting Classifier outperforms the other models tested by a significant margin (96 per cent). This model's AUC-ROC of 0.97 indicates it does a decent job at separating the positive and negative categories. 2023 IEEE.
Basha M.S.A.; Prabhavathi C.; Khangembam V.; Sucharitha M.M.; Oveis P.M.
2023 2nd International Conference for Innovation in Technology, INOCON 2023
https://doi.org/10.1109/INOCON57975.2023.10101206 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158065732&doi=10.1109%2fINOCON57975.2023.10101206&partnerID=40&md5=7efe6d8c3369ac23edaa0bd94dba7ce0
Basha M.S.A., Gandhi Institute of Technology and Management (Deemed to Be University), GITAM School of Business, Bengaluru, India; Prabhavathi C., Deemed to Be University, Department of Professional Studies Christ, Bengaluru, India; Khangembam V., Deemed to Be University, Department of Professional Studies Christ, Bengaluru, India; Sucharitha M.M., Deemed to Be University, Department of Professional Studies Christ, Bengaluru, India; Oveis P.M., Gandhi Institute of Technology and Management (Deemed to Be University), GITAM School of Business, Bengaluru, India
Can Artificial Intelligence Accelerate and Improve New Product Development
Artificial Intelligence; Business process; Marketing; New Product Development
Today, AI have successfully set up a good foundation in a broad scope of business processes. Associations including AI for product headway processes have uncovered more huge yields on hypotheses, better viability in their cycles, and effective utilization of resources. A sensible headway framework is paramount for capable product development, especially for complex endeavours. AI thinking is in like manner improving new product development. AI is probably going to experience clients in numerous areas. New yield evolution as in collaboration utilizes its capital and capacities to make another item or work on a current one. Product development is viewed as one among the fundamental cycles for progress, endurance, and recharging of associations, especially for firms in, by the same token, quick-moving or cutthroat business sectors. AI assists people's lives by expanding connections creating and multiplying items that can work with individuals' daily exercises in quite a large number of areas. Consequently, the impact of involving Artificial Intelligence for new developments is to induce things simpler. This paper attempts to outline the acceleration of new product development with the help of artificial intelligence technology. This study addressed the tailored AI in product improvement and product development transformation. Lastly, this article points out how AI accelerates product development and future outlook. 2023 American Institute of Physics Inc.. All rights reserved.
Basha M.S.A.; Raju R.P.K.; Tuteja S.; Maheshwari A.
AIP Conference Proceedings, Vol-2821, No. 1
https://doi.org/10.1063/5.0158418 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179816359&doi=10.1063%2f5.0158418&partnerID=40&md5=ac99438972f2c81481ec212f12d988d2
Basha M.S.A., GITAM School of Business, GandhiInstitute of Technology and Management (Deemed to be University), Karnataka, Bengaluru, 561203, India; Raju R.P.K., GITAM School of Business, GandhiInstitute of Technology and Management (Deemed to be University), Karnataka, Bengaluru, 561203, India; Tuteja S., Indira Institute of Management, Maharashtra, Pune, 411033, India; Maheshwari A., Department of Professional Studies, CHRIST (Deemedto be University), Karnataka, Bengaluru, 560029, India
Mapping the Landscape of Business Intelligence Research: A Bibliometric Approach
The integration of Business Intelligence (BI) is an essential element in contemporary enterprises, facilitating the conversion of voluminous data into valuable insights to support informed decision-making. Consequently, a considerable body of literature has been devoted to investigating the utilization of Business Intelligence (BI) in enhancing company efficiency and competitiveness. The present investigation employs bibliometric methods as a means to examine the research pertaining to Business Intelligence (BI). This includes an examination of the main writers and universities, publication patterns, and the intellectual framework of the domain. This investigation centers on the timeframe spanning from 2000 to 2022 and scrutinizes a corpus of 3729 Scopus articles pertaining to business intelligence. The findings suggest that the domain of Business Intelligence (BI) has experienced a substantial expansion recently. The study's results reveal significant contributors, establishments, nations, and references in the discipline, along with developing research patterns and prospects for further investigation. In general, this research emphasizes the significance of bibliometric evaluation as a means of comprehending the present status of BI research and discovering approaches to enhance the utilization of BI in contemporary organizational decision-making procedures. This study has the potential to provide valuable insights into the present state of research within the field, pinpoint significant trends and themes, and highlight potential avenues for future research. 2023 IEEE.
Basha M.S.A.; Raju R.P.K.; Uma Devi A.; Raja S.; Maheshwari A.
7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings
Basha M.S.A., Gitam School of Business, Gandhi Institute of Technology and Management, Deemed to Be University, Bengaluru, India; Raju R.P.K., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Uma Devi A., Srm Valliammai Engineering College, Department of Management Studies, Chengalpattu, India; Raja S., School of Management, Vel-Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology, Avadi, India; Maheshwari A., School of Commerce, Finance and Accountancy Christ (Deemed to Be University), Ghaziabad, India