KDV PRASAD
@sibmhyd.edu.in
Professor (Reserch)
Symbiosis Institute of Business Management, Hyderabad
Professor KDV Prasad, works as a Faculty of Research in Symbiosis Institute of Business Management, Hyderabad; Symbiosis International (Deemed University), Pune, India. Professor Prasad holds Masters in Computer Applications and Masters in Software Systems from BITS, Pilani; MBA (Human Resources), IGNOU, New Delhi.. Professor Prasad Possess PhD in Business Management (Kanpur University); and PhD in Business Administration (RTM Nagpur University. He is AIMA certified Management Teacher, Fellow, World HR Board, Carlton Advanced Management Institute, USA. Professor Prasad Published over 200 articles in scopu/WOS indexed journals, and 3 books.
RESEARCH, TEACHING, or OTHER INTERESTS
Business, Management and Accounting, General Business, Management and Accounting, Arts and Humanities, Management Science and Operations Research
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
K. D. V. Prasad, Sripathi Kalavakolanu, Tanmoy De, and V. K. Satyaprasad
Springer Science and Business Media LLC
AbstractMoonlighting as a practice, the limelight was gained during the COVID-19 pandemic due to remote work involving flexible work, which saved employees’ commuting time to the office and has become a potential source of income for individuals seeking other jobs. The authors examined the phenomenon of moonlighting by assessing the relationships between job satisfaction, organizational commitment, and moonlighting intentions. The authors also examined the mediating effects of employee organizational commitment and economic intentions and the moderating role of human resource practices on the relationship between job satisfaction and moonlighting intentions. The data were gathered for five reflective constructs of this empirical study—job satisfaction, organizational commitment, human resources practices, economic intentions, and moonlighting intentions—by surveying IT-enabled industry employees in Hyderabad. The data from 311 valid responses were subjected to structural equation modeling analysis using IBM AMOS version 28. The model-fit indices from SEM analysis indicate excellent model fit. The structural model from SEM analysis reveals that 50% of the variance in moonlighting is accounted for by job satisfaction and organizational commitment. The factor of job satisfaction is statistically significant and influences the moonlighting intentions of employees in IT-enabled industries. Job satisfaction has a positive impact on organizational commitment, and when organizational commitment increases, moonlighting intentions decrease. Organizational commitment partially mediates moonlighting intentions through job satisfaction. The study also assessed the moderating role of human resource practices on the relationship between job satisfaction and moonlighting intentions. The moderation analysis results reveal statistically significant and positive moderating effects of human resource practices on intentions to moonlight through job satisfaction. The slope analysis indicated that human resource practices strengthen the positive relationship between job satisfaction and moonlighting.
Shatha Ghazi Felemban, Farag M.A. Altalbawy, Irfan Ahmad, Abhinav Kumar, Carlos Rodriguez-Benites, Ahmed Hjazi, K.D.V. Prasad, Anaheed Hussein Kareem, Ahmed Hussein Zwamel, and Shahin Ramazi
Elsevier BV
Davood Ghorbanzadeh, K. D. V. Prasad, Natalia Alekseevna Prodanova, Iskandar Muda, Joko Suryono, and Nafisa Yuldasheva
Springer Science and Business Media LLC
Dankan Gowda V., Anjali Sandeep Gaikwad, Aparna Atul Junnarkar, K. D. V. Prasad, and Sofia Rani Shaik
IGI Global
In a landscape of technology that is changing at breakneck speed, the combination of the internet of things (IoT) with robotics represents an important step toward future automation and intelligent systems. In this chapter, the authors explain how such an integration will change things. This combination of IoT's connected devices and robotic dynamism brings unprecedented progress in many areas, such as industrial automation and healthcare. After cutting through the technical jumble and coming to terms with plausible applications, problems, and opportunities afforded by this match, readers will have a clear understanding of what this revolutionary field has today, as well as its promise for tomorrow.
Dankan Gowda V., Kirti Rahul Kadam, Nazeer Shaik, K. D. V. Prasad, and Sofia Rani Shaik
IGI Global
In the face of the development of robotic technology, integration with cloud technologies is a big opportunity to greatly improve robots. This chapter will synthetize the spirit which robotic applications lacks to refine and add depth to it. Also, this procedure not only gives the robotic device more computing power, but also ups its storage capacities and resource management, all without rendering robotical atrophy. In this chapter, the authors examine several different forms of application running away from home. In particular, with the growing public interest in robotics, it is vital that not only participants from industry but also researchers and developers who should realize that through cloud-based offloading they really have a chance to create a revolution.
V. Dankan Gowda, Y. N. Sunitha, Sadashiva V. Chakrasali, K. D. V. Prasad, Parismita Sarma, and Mirzanur Rahman
Taru Publications
Accurate diagnosis and well-informed treatment choices are only possible with the help of sophisticated medical imaging technology. The introduction of digital imaging methods has led to a dramatic rise in the volume of data associated with medical images, which in turn has led to difficulties in their storage, transmission, and administration. It is crucial to utilize effective image compression techniques to address these challenges without compromising the diagnostic integrity of the pictures. The wavelet transform has matured into a potent method for striking a good compromise between picture quality and file size reduction while compressing. The safe and efficient transfer of medical image data is a major concern in today’s healthcare settings. In this academic investigation, we investigate how wavelet transform-based techniques may be used to enhance medical picture compression. The proposed techniques optimize compression ratios while maintaining diagnostic picture quality by making use of the wavelet transform’s multi-resolution and frequency localization features. To address the unique challenges given by medical image collections, various iterations of the wavelet transform and compression techniques are investigated. Through a series of detailed tests involving several medical picture modalities, the effectiveness of these technologies is thoroughly evaluated, demonstrating their effectiveness in achieving significant data reduction without sacrificing clinical information.
V. Dankan Gowda, Pratik Gite, Mirzanur Rahman, Kirti Rahul Kadam, G. Manivasagam, and K. D. V. Prasad
Taru Publications
Biometric systems have taken the front seat as having core foundations in 21stcentury digital information technology, biometric systems hold a position therein or authentication processes. But the growing complexity of cyber threats requires greater security practices. This paper presents an innovative biometric security approach based on the combination of ECC and more advanced session keys. The goal is to strengthen biometric systems against sophisticated cyber threats ensuring fast and effective authentication methods. The proposed approach takes advantage of ECC’s strength in generating secure biometric data along with session keys dynamically to build a more resilient, yet flexible, security model. It is shown on the simulations that security metrics increase significantly in terms of resistance to common cryptographic attacks and data breaches without decreasing the system performance. This study describes not only the current vulnerabilities in biometric security systems towards more secure and reliable authentication systems by the convergence of biometric data to advanced cryptographic techniques.
V. Dankan Gowda, Sajja Suneel, Mirzanur Rahman, Gobinda Chandra Das, R. Senthil Kumar, and K. D. V. Prasad
Taru Publications
In the digital era, authentication system’s security is crucial. This paper discusses the issue of improving digital authentication from the position of cryptographic improvements in intrinsic verification structures. The emphasis is made on creation and implementation of efficient cryptographic protocols that reinforce security in digital authentication procedures. This research focuses on developing a new cryptographic framework that is capable of improving the attack tolerance of intrinsic verification systems against various kinds of cyber-attacks such as identity theft and data breach. This structure takes advantage of innovative cryptographic algorithms, including quantum-resistant techniques that secure authentication records containing sensitive information. The Author conducts further feasibility study of these innovations that can be applied in practical settings, quantifying the success or failure through simulations. The outcomes evidenced significant security features improvements, indicating that such cryptographic developments have the potential to revolutionize digital authentication. This study is not only coming up with the solution, Prescence to current security challenges but also has shown path for development of secure digital authentication system in an interconnected world.
Chou‐Yi Hsu, Abdullah Ali Alzahrani, Hatem Ghaleb Maabreh, K. D. V. Prasad, Dmitry O. Bokov, Anaheed Hussein Kareem, Ahmed Alawadi, Ali Ihsan, Maha Noori Shakir, and Mohammed Qasim Alasheqi
Wiley
AbstractFindings on the effect of walnut consumption on endothelial function are conflicting. Therefore, the present systematic review and meta‐analysis summarized available trials in this regard. A systematic search was performed in online databases including PubMed‐Medline, Scopus, and ISI Web of Science up to October 2023. Articles that reported the effect of walnut intake on flow‐mediated dilation (FMD), intercellular adhesion molecule‐1 (ICAM‐1), vascular cell adhesion molecule‐1 (VCAM‐1), and stimulus‐adjusted response measure (SARM) were included. Random effects models for a weighted mean difference (WMD) or standardized mean difference (SMD) were used to test for the overall effect. Six eligible trials were analyzed (250 participants). Walnut intake significantly increased FMD (WMD: 0.94%, 95% CI: 0.12 to 1.75; p = 0.02). However, meta‐analysis could not show any beneficial effect of walnut intake on ICAM‐1 (SMD: −0.23, 95% CI: −0.68 to 0.22; p = 0.31), VCAM‐1 (SMD: −0.02, 95% CI: −1.38 to 1.34; p = 0.97), and SARM (WMD: 0.01%, 95% CI: −0.01 to 0.04; p = 0.28). In conclusion, the present meta‐analysis suggests that walnuts may reduce cardiovascular disease risk by improving FMD. However, further studies should be performed on adults to determine the effect of walnut intake on endothelial function.
V. Mahalakshmi, Mukta Sandhu, Mohammad Shabaz, Ismail Keshta, K.D.V. Prasad, Nargiza Kuzieva, Haewon Byeon, and Mukesh Soni
Elsevier BV
G. U. Vasanthakumar, V. Dankan Gowda, Prabhakar S. Manage, K. D. V. Prasad, and Venkatesan Hariram
IGI Global
The extensive use of clinical decision support systems (CDSS) and electronic health records (EHR) has significantly altered the landscape of healthcare. Medical professionals now have access to priceless tools that transform patient data management and help them make wise clinical judgments. However, as we seamlessly incorporate artificial intelligence (AI) solutions into EHR and CDSS, a new era of healthcare is beginning. The incorporation of AI technologies is thoroughly explored in this chapter, shedding light on how they might improve clinical operations and patient outcomes. The chapter opens by emphasizing the crucial role played by EHR in centralizing medical records, digitizing patient data, and enabling effective data sharing between healthcare providers. The chapter conducts an in-depth exploration of how machine learning algorithms are applied to unearth patterns in patient data, identify disease risks, and provide personalized treatment recommendations.
Kottala Sri Yogi, Dankan Gowda V, Mouna K M, L.R. Sujithra, KDV Prasad, and P Midhun
IEEE
In the modern era of digital communication, social media platforms are data factories and hence provide a new set of opportunities as well as challenges before the analysts. The objective of this paper is to evaluate the scalability and performance of various ML algorithms for social media data analysis in practice with high-volume datasets. The objective is to design ML algorithms that are scalable and achieve high accuracy when used with big datasets. In a literature survey, observed gaps in the present research advancements toward scalability and performance analysis for various ML techniques used for this intent. To make sure of diversity and size, we collected a big source from different social networks. The approach we did involve assessing a number of ML methods that are widely known like natural language processing (NLP), sentiment analysis (SA) and predictive analytics on scalability factors such as time to process and resource utilization while also considering performance metrics such as accuracy precision recall rate f1 score. The results demonstrate significant differences in the scalability of investigated approaches and their quality, such as some algorithms being more efficient or accurate at a large scale. This research adds value to the existing works in this area, by performing a detailed comparative study of ML techniques for large-scale social media data processing considering scalability vs performance tradeoffs. The findings are informative, for the research and practice community, on selection of suitable ML techniques that will be applicable to social media analytics; scalability is an important factor in this era dominated by big data. In future research, more advanced techniques and hybrids should be taken into account to enhance the scalability and performance of social media data analysis.
V. Dankan Gowda, D. Palanikkumar, Sajja Suneel, Satish Dekka, T. Thiruvenkadam, and K. D. V. Prasad
Taru Publications
There is need to develop and create new data protection mechanisms aimed at the compensating for the lack of security of WSNs. This paper introduces new cryptographic image-based techniques aimed at improving data security in WSNs. The solution also integrates imaging processing techniques with advanced cryptographic principles to merge a two-layered security architecture. This work provides a new WSN-specific encryption algorithm that delivers low energy consumption and smaller computational burden. Likewise, this is connected to an image-based authentication approach which employs steganographic methods in the information hiding technique. Intensive simulations allow the approach that supports methodology, naming increased security metrics obtained without decreasing in network performance. This study seeks to fill the gap in current practice in WSN security and provides a foundation for future research on image-based techniques integrated with cryptographic algorithms aiming at more secure data protection under limited resources.
Davood Ghorbanzadeh, Ahmad Qasim Mohammad AlHamad, Kuicthok Yak Deng, Ahmed Alaa Hani Alkurdi, K. D. V. Prasad, and Mohsen Sharbatiyan
Springer Science and Business Media LLC
V Dankan Gowda, Avinash Sharma, Kdv Prasad, Rini Saxena, Tarkeshwar Barua, and Khalid Mohiuddin
IEEE
As natural and manmade disasters grew in number, as a result the problem of how to quickly and effectively respond to disaster has become fresh. This is precisely the purpose of this research: Using IoT technologies and powerful data analysis techniques, to integrate them into existing disaster management systems. The beginning of the article contains a broadcasted statement to the effect that traditional methods are insufficient, and real-time data and preemptive response systems are needed. With the application of Internet of Things, an integrated system is proposed in which countless types of information such as weather conditions and seismic activity are gathered by sensors and actuators. Advanced machine learning algorithms and predictive modeling are used to analyze the gathered data. This allows us to make real-time decisions. The design and construction of an IoT-based disaster management system is the methodology behind the research. In particular, we will evaluate how effective it is at reducing response times and increasing overall resilience to disasters. The results show a high efficiency in response, which reflects the feasibility of the method. Finally, the paper discusses the problems encountered in implementing IoT and advanced data analysis of disaster risk and suggests future research avenues. There is no doubt that they will change the present disaster management practice forever.
Davood Ghorbanzadeh, Kiran Nair, Teddy Chandra, Jamshid Bakhtiyorovich Ergashev, and K. D. V. Prasad
Informa UK Limited
Bhawani Sankar Panigrahi, Nagabhooshanam Nagarajan, Kanaka Durga Veera Prasad, Sathya, Satish Sampatrao Salunkhe, Pilli. Dharmendra Kumar, and Muthevi Anil Kumar
Springer Science and Business Media LLC
Mohammad Yasin Zamanian, Mehraveh Sadeghi Ivraghi, Reena Gupta, K. D. V. Prasad, Hashem O. Alsaab, Beneen M. Hussien, Hazem Ahmed, Montather F. Ramadan, Maryam Golmohammadi, Nikta Nikbakht,et al.
Wiley
AbstractParkinson's disease (PD) is a neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra, leading to various motor and non‐motor symptoms. Several cellular and molecular mechanisms such as alpha‐synuclein (α‐syn) accumulation, mitochondrial dysfunction, oxidative stress and neuroinflammation are involved in the pathogenesis of this disease. MicroRNAs (miRNAs) play important roles in post‐transcriptional gene regulation. They are typically about 21–25 nucleotides in length and are involved in the regulation of gene expression by binding to the messenger RNA (mRNA) molecules. miRNAs like miR‐221 play important roles in various biological processes, including development, cell proliferation, differentiation and apoptosis. miR‐221 promotes neuronal survival against oxidative stress and neurite outgrowth and neuronal differentiation. Additionally, the role of miR‐221 in PD has been investigated in several studies. According to the results of these studies, (1) miR‐221 protects PC12 cells against oxidative stress induced by 6‐hydroxydopamine; (2) miR‐221 prevents Bax/caspase‐3 signalling activation by stopping Bim; (3) miR‐221 has moderate predictive power for PD; (4) miR‐221 directly targets PTEN, and PTEN over‐expression eliminates the protective action of miR‐221 on p‐AKT expression in PC12 cells; and (5) miRNA‐221 controls cell viability and apoptosis by manipulating the Akt signalling pathway in PD. This review study suggested that miR‐221 has the potential to be used as a clinical biomarker for PD diagnosis and stage assignment.
Manda Damodhar Rao, Yellapu Venkata Narayana, and Varre Venkata Kanaka Durga Vara Prasad
Frontier Scientific Publishing Pte Ltd
<p>In Very Large Scale Integration (VLSI) design, device scaling is restricted due to subthreshold swing limitations and short channel effects. This article discusses the role of different power reduction techniques in the implementation of 14 nm fin-shaped field-effect transistor (FinFET) centered static random access memory (SRAM) cells with good subthreshold swing and reduced short channel effects (SCE). Dynamic threshold and power gating are incorporated in SRAM cells to advance the memory cell performance by reducing static power dissipation in standby mode. The power analysis was performed on different SRAM cells with different transistor count i.e., 6, 7, 8, and 9. The performance of SRAM cell is analysed in power dissipation and is reduced by 20% using power gating method and in dynamic threshold it is reduced to nano watts due to less leakage power.</p>
Latika Jindal, Aditi Sharma, K.D.V. Prasad, Azeem Irshad, Richard Rivera, and Abdurakhimova Dilora Karimovna
Elsevier BV
KDV Prasad, Alaa Aladini, Nozliya Zarilovna Normurodova, and Barana Belton
Springer Science and Business Media LLC
AbstractMany different elements have a significant impact on the effectiveness of both teaching and assessment. The ever-expanding body of research on assessment has provided an overview of several cognitive, social, and emotional aspects that impact the academic accomplishment of learners in either a direct or indirect manner. In spite of the promising literature on assessment, no research has been done to investigate the probable interactions between self-assessment (SA), test-taking skills (TTS), student evaluation apprehension (SEA), self-esteem (S-E), and language achievement (LA). The purpose of this research was to offer a model that would illuminate the connection among SA, TTS, SEA, S-E, and LA in online classes. Therefore, 417 Turkish EFL students were given the Core of Self-assessment Questionnaire (CSAQ), the Test-Taking Skills Scale (TTSS), the Student Evaluation Apprehension Scale (SEAS), and the Foreign Language Learning Self-esteem Scale (FLLSE). Structural equation modeling (SEM) and confirmatory factor analysis (CFA) both found that SA and TTS are related to SEA, S-E, and LA. Based on the data analysis, EFL students’ SA and TTS can predict the level of SEA, S-E, and LA in online classes. This has important implications for the development of EFL students’ feeling of effectiveness and their ability to define and achieve goals. Consequences and potential future directions are also examined.
Abbas Abdollahi, K. D. V. Prasad, Nasser Said Gomaa Abdelrasheed, Shadia Hamoud Alshahrani, Sarah Jawad Shoja, Ghaidaa Raheem Lateef Al-Awsi, Edwin Gustavo Estrada-Araoz, Nermeen Singer, Andrés Alexis Ramírez-Coronel, Yasser Fakri Mustafa,et al.
Springer Science and Business Media LLC
Abstract Background Previous studies have shown that physical appearance perfectionism could play an important role in social physique anxiety; however, the moderating role of body compassion has not been studied. The current study aims to explore the moderating role of body compassion in the relationship between physical appearance perfectionism and social physique anxiety in undergraduate students. Methods A sample of 418 undergraduates (n = 418; 217 female and 201 males) from three universities in Tehran, Iran completed online questionnaires measuring physical appearance perfectionism, body compassion and social physique anxiety. Results The results of structural equation modeling showed that physical appearance perfectionism (β = 0.68, p < 0.001) positively predicted the social physique anxiety and body compassion negatively predicted (β = − .56, p < 0.001) the social physique anxiety in undergraduate students. A multi-group analysis showed that body compassion acted as a moderator between physical appearance perfectionism and social physique anxiety. Conclusions The results suggested that individuals with greater levels of physical appearance perfectionism are more likely to experience social physique anxiety. Also, the results suggested that individuals who were at a high level of the body-compassion group experienced lower levels of social physical anxiety if they also had high levels of physical appearance perfectionism. Therefore, body-compassion acted as a protective role in the relationship between physical appearance perfectionism and social physique anxiety.
Sayed M. Ismail, Iman Nikpoo, and K. D. V. Prasad
Springer Science and Business Media LLC
AbstractThe present research tried to prompt self-regulated learning, autonomy, and self-efficacy of EFL learners through authentic assessment in Iranian EFL classrooms. To do so, 57 Iranian EFL learners were chosen and assigned to two equal groups; the Experimental Group (CG) and the Control Group (CG). Then, three questionnaires were administered to assess the respondents’ self-regulated learning, autonomy, and self-efficacy before doing the treatment. After that, the EG received the treatment via using authentic assessments whereas the CG received the instruction through non-authentic assessments. After teaching 15 English passages to both groups, three post-tests were administered to them to determine the effects of the treatment on their self-regulated learning, autonomy, and self-efficacy. Lastly, the attitude questionnaire was given to the EG students to assess their attitudes toward implementing authentic assessments in EFL classes. The outcomes of ANCOVA revealed that there were significant differences between the post-tests of the EG and the CG. The results displayed that the EG noticeably outdid the CG in self-regulated learning, autonomy, and self-efficacy post-tests. In addition, the results showed that the EG held a positive attitude toward using authentic assessment in EFL classes. It can be concluded that applying authentic assessment in EFL classes can bring about constructive impacts for EFL learners. The findings of this investigation can be extremely valuable for EFL teachers and material designers to consider the impacts of authentic assessments and invest more in using these sorts of assessments.
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3 Patents
Industry, Institute, or Organisation Collaboration
International Crops Research Institute for Semi-Arid Tropics, Hyderabad India over 30 years research administration experience
Symbiosis Institute of Business Management, Hyderabad as Research Professor 2 Years of experience
INDUSTRY EXPERIENCE
International Crops Research Institute for Semi-Arid Tropics, Hyderabad India over 30 years research administration experience