o Narrativeattentiveness of Routing Algorithms for Mobile Ad - Hoc Networks
o Explore challenging intend characteristics of 4G, 5G, WSN and VAN
o Significance of Cyber Security and Digital Forensics at current arena
o Predictive Data Analytics using Machine Learning and Deep Learning
o Effectiveness of Load Balancing and Virtualization in Cloud Computing Environment
100
Scopus Publications
Scopus Publications
Fine-tuning XLNet for Amazon review sentiment analysis: A comparative evaluation of transformer models Amrithkala M. Shetty, Manjaiah D. H., Mohammed Fadhel Aljunid ETRI Journal, 2026 Transfer learning in large language models adapts pretrained models to new tasks by leveraging their existing linguistic knowledge for domain‐specific applications. A fine‐tuned XLNet, base‐cased model is proposed for classifying Amazon product reviews. Two datasets are used to evaluate the approach: Amazon earphone and Amazon personal computer reviews. Model performance is benchmarked against transformer models including ELECTRA, BERT, RoBERTa, ALBERT, and DistilBERT. In addition, hybrid models such as CNN‐LSTM and CNN‐BiLSTM are considered in conjunction with single models such as CNN, BiGRU, and BiLSTM. The XLNet model achieved accuracies of 95.2% for Amazon earphone reviews and 95% for Amazon personal computer reviews. The accuracy of ELECTRA is slightly lower than that of XLNet. The exact match ratio values for XLNet on the AE and AP datasets are 0.95 and 0.94, respectively. The proposed model achieved exceptional accuracy and F1 scores, outperforming all other models. The XLNet model was fine‐tuned with different learning rates, optimizers (such as Nadam and Adam), and batch sizes (4, 8, and 16). This analysis underscores the effectiveness of the XLNet approach for sentiment analysis tasks.
Task Offloading and Resource Allocation Using the Echo Tracking Optimization Enabled QoS-Aware Scheduling for MEC-Enabled WBAN Healthcare System Shaik Afzal Ahammed M S, Manjaiah D H Transactions on Emerging Telecommunications Technologies, 2025 In the context of the Internet of Medical Things (IoMT), the rapid expansion of wearable medical devices and healthcare data presents tremendous challenges related to the improved Quality of Service (QoS) and computing task offloading for Smart healthcare systems. Further, the Mobile Edge Computing (MEC)‐enabled healthcare systems, which allow computation offloading to edge servers nearby, are attracting great attention as a result of the extraordinary development in Wireless Body Area Network (WBAN) users and applications based on 5G. However, the existing systems in MEC‐enabled WBAN‐based healthcare systems produce too many control frames while transmitting data, resulting in increased latency, energy wastage, and a lack of flexibility. Therefore, this research aims to design a routing algorithm in WBAN that efficiently allocates resources and consumes less energy utilizing the Echo Tracking Optimization‐based MEC‐enabled WBAN systems. Specifically, the proposed model provides ultra‐reliable data transfer and processing with extremely low latency and energy consumption to meet the demands of healthcare services and applications. More effectively, the proposed approach exploits the Echo Tracking Optimization (ETO) that handles the resource allocation and enhances the QoS by addressing the problem of selection of the target tasks on analyzing the medical criticality, highest relative computing capacity, and energy constraints for effective task offloading. Compared to the other existing techniques, the proposed ETO‐QoS aware scheduling effectively lowers latency and energy consumption while increasing throughput and overall WBAN utilization by reporting a delay of 0.102 ms, energy loss of 7.523 J, packet loss of 95, and throughput of 0.723 Kbps outperforming the other existing techniques.
AI and Data Science in Business Services: Enhancing Efficiency and Driving Innovation Advanced Digital Technologies in Financial and Business Management Unleashing the Power of Artificial Intelligence Machine Learning Blockchain and the Internet of Things, 2025
NOVEL FINE-TUNED BIDIRECTIONAL GRU AND FASTTEXT EMBEDDINGS FOR LOCATION-BASED SENTIMENT ANALYSIS AND PREDICTIONS Akshatha Shetty, Dr. Manjaiah D H, Praveena Kumari M. K. Interdisciplinary Journal of Information Knowledge and Management, 2025 Aim/Purpose: The need for this paper stems from the challenge of efficiently analyzing large volumes of customer reviews in the hotel industry, which is growing and complex due to the widespread use of digital platforms. With consumers increasingly sharing feedback across various social media applications, manual processing becomes impractical, necessitating machine learning algorithms for accurate sentiment analysis and prediction. Background: This paper addresses the problem by applying machine learning algorithms, specifically fine-tuned bidirectional GRU with FastText embedding, to perform location-based sentiment analysis and prediction of customer reviews in the hotel industry, providing an efficient solution to process large-scale unstructured data and extract valuable insights for improving customer experience. Methodology: This study employs machine learning techniques, including Random Forest (RF), Support Vector Machine (SVM), Bidirectional Long-Short Term Memory (BiLSTM), and Bidirectional Gated Recurrent Unit (GRU), to perform sentiment analysis on customer reviews in the hotel industry. The paper focuses on using a fine-tuned bidirectional GRU model with FastText embedding for location-based sentiment analysis and prediction. The research sample consists of a large dataset of customer reviews, which are processed and analyzed to predict sentiment and evaluate model performance, achieving an overall score of 84.31%. Contribution: This paper contributes to the body of knowledge by demonstrating the effective application of advanced machine learning algorithms, particularly a fine-tuned bidirectional GRU with FastText embedding, for sentiment analysis in the hotel industry. It offers a unique approach to location-based sentiment analysis, allowing for a deeper understanding of regional variations in customer perceptions. The study also provides insights into the comparative effectiveness of different machine learning models for sentiment classification and prediction by comparing multiple algorithms. This work enhances the existing methodologies for processing large-scale, unstructured data and highlights the potential of sentiment analysis to drive data-informed strategies in customer-centric industries. Findings: The study found that the fine-tuned bidirectional GRU model with FastText embedding achieved an accuracy of 84.31%, outperforming other algorithms in sentiment classification. It highlighted the effectiveness of location-based sentiment analysis for understanding regional variations in customer perceptions. The approach also demonstrated strong predictive capabilities, aiding data-driven decision-making in the hospitality sector. Recommendations for Practitioners: Practitioners in customer-focused sectors, particularly hospitality, should consider using advanced models like the fine-tuned bidirectional GRU with FastText embedding for precise sentiment analysis. This approach helps capture regional customer preferences, allowing for tailored services. Integrating sentiment insights with real-time data can further enhance responsiveness to customer needs, supporting data-driven improvements in customer satisfaction. Recommendation for Researchers: Researchers should investigate more advanced models and varied datasets to enhance sentiment analysis accuracy and applicability. Extending sentiment analysis to fields like healthcare and finance could offer broader insights into consumer behavior. Additionally, integrating sentiment analysis with real-time data sources, such as social media, may yield more dynamic predictive models and reveal key regional trends. Impact on Society: The findings of this paper have significant implications for industries, particularly in the hospitality sector, as they provide an effective method for analysing customer sentiment at a granular level. The ability to assess customer opinions through sentiment analysis can help businesses improve customer service, tailor their offerings to meet customer needs, and enhance the overall customer experience. Additionally, location-based sentiment analysis can enable businesses to understand regional differences, allowing for more personalized marketing strategies and better resource allocation. Future Research: Future research could focus on improving the accuracy and efficiency of sentiment analysis models by incorporating more advanced deep learning techniques and larger, more diverse datasets. Exploring the application of sentiment analysis in other sectors, such as healthcare, education, and finance, could provide broader insights into customer perception. Additionally, integrating sentiment analysis with other data sources, such as social media and customer feedback, may lead to more comprehensive and real-time prediction models. Further directions include multilingual sentiment analysis, the use of transformer-based models, and deployment in resource-constrained environments.
Artificial Intelligence: A New Hope in Agriculture Giddaluru Somasekhar, Kotagiri Srujanraju, Manjaiah D. Huchaiah, Nuthanakanti Bhaskar Artificial Intelligence for Smart Cities and Villages Advanced Technologies Development and Challenges, 2022
Performance of reinforcement learning model with boltzmann machine for improving the intrusion detection system in Manet International Journal of Scientific and Technology Research, 2020
Privacy preserving scheme for MapReduce Madhvaraj M. Shetty, D. H. Manjaiah Proceedings of IEEE International Conference on Circuit Power and Computing Technologies Iccpct 2017, 2017
Data security in Hadoop distributed file system Madhvaraj M Shetty, D. H. Manjaiah Proceedings of IEEE International Conference on Emerging Technological Trends in Computing Communications and Electrical Engineering Icett 2016, 2017
A cloud forensic strategy for investigation of cybercrime Ezz El-Din Hemdan, D. H. Manjaiah Proceedings of IEEE International Conference on Emerging Technological Trends in Computing Communications and Electrical Engineering Icett 2016, 2017
A survey of various scheduling algorithms in cloud environment B. Santhosh, D. H. Manjaiah, L. Padma Suresh Proceedings of IEEE International Conference on Emerging Technological Trends in Computing Communications and Electrical Engineering Icett 2016, 2017
A new scheme for IPv6 BD-TTCS translator J. Hanumanthappa, D. H. Manjaiah Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012
Divide- and-conquer based IPv6 Address LPR in BD-SIIT IPv4/IPv6 transition using a novel reduced segment table(RST) algorithm Recent Researches in Computers and Computing International Conference on Computers and Computing Iccc 11, 2011
