Usha Divakarla

Scopus Publications

“RaagaDhvani: A novel augmented multi-feature dataset: Advancing emotion recognition in Carnatic music with multimodal features and hybrid deep learning”
Mrs. Archana Priyadarshini, Dr. Usha Divakarla
Data in Brief, 2026
dataset has been created, focusing on culturally significant, emotion-driven vocal and flute renditions of eleven carefully selected Carnatic ragas, which are chosen for their psychological and affective associations. The dataset comprises 11 classical ragas, each recorded for approximately 5 min in high-quality audio. This approach preserves essential musical features, including gamakas, phrase structures, and tonal motifs, while generating sufficient training samples for deep learning models. The 448 ragas dataset is further enhanced with data augmentation, including pitch shifting, time-stretching, and noise addition. Later, to increase the validity and quality of Carnatic music, self self-curated dataset of 165 files are augmented with pitch shift and time stretch, resulting in 825 files to increase variability and improve model generalization. This dataset establishes a strong foundation for computational analysis of Carnatic music, supporting tasks like raga classification, emotion recognition, and multimodal retrieval. By integrating audio features with emotion annotations, it advances deep learning-based emotion prediction and fosters applications in psychology and music therapy.
Utilizing Artificial Intelligence for the Identification of Plant Species and Detection of Diseases through Deep Learning
Usha Divakarla, K. Chandrasekaran, Srihari Govindarajan, Sivaramakrishnan Sangameswaran
Decision Sciences in Bioinformatics Theory and Practice, 2026
Utilizing deep learning for the identification of plant species and the detection of diseases is crucial in mitigating the impact of these diseases on plant growth and crop yield. Early diagnosis is key to preventing losses and enhancing the overall quality and quantity of agricultural products. While various machine learning models have been employed for disease detection, the advent of deep learning has significantly increased the potential for improved accuracy in this research domain. Our proposed system aims to identify both the plant species and diseases affecting the leaves. A test database containing various plant diseases was established to assess the accuracy of our model. Training the classifier with the obtained data allows for more accurate predictions. We employed the GoogleNet model, which incorporates different layers designed to predict diseases effectively. In contrast to the conventional method where farmers rely on visual inspection and their knowledge of plant diseases, our system offers a more efficient and accurate alternative. Large-scale manual inspections are time-consuming, challenging, and often lack precision. Seeking expert consultation incurs substantial costs. To address these challenges, we implemented techniques involving automatic disease detection devices, enhancing accuracy while reducing costs and simplifying the process. This review comprehensively elucidates the utilization of deep learning models for visualizing various plant diseases. Additionally, it highlights research gaps, aiming to bring greater clarity to the early detection of plant diseases, even before symptoms become apparent.
Detecting Lung Anomalies Through Transfer Learning-Based Deep Learning Based X-Ray and MRI Image Fusion
Usha Divakarla, Srushti, Varshini H C, Yukti S
Ccic 2026 Contemporary Computing Innovations Conference 2026, 2026
Stride-based threat modeling for blockchain-based healthcare supply chain management system
Blockchain Enabled Internet of Things Applications in Healthcare Current Practices and Future Directions, 2025
A Comprehensive Framework for Safeguarding Big Data Applications Against Ransomware Attacks
Usha Divakarla, K Chandrasekaran
Proceedings of IEEE International Conference for Women in Innovation Technology and Entrepreneurship Icwite 2025, 2025
Big data systems are essential in applications like business intelligence, predictive analytics, and machine learning, but their vast repositories of sensitive information make them prime targets for ransomware attacks. Ransomware encrypts data, demanding a ransom for its release, and traditional countermeasures often fail to mitigate its impact effectively in big data environments due to performance overheads or vulnerabilities to advanced attacks. This paper proposes a robust framework to defend big data applications against ransomware. The approach leverages entropy-based dynamic detection to identify ransomware through anomalous write patterns, complemented by machine learning techniques to classify potential threats. By identifying and securing the most vulnerable servers, the framework minimizes performance degradation while ensuring enhanced protection. Additionally, privilege-escalation attacks are mitigated through randomized security identifiers, and backup systems are augmented with ransomware detection to provide a comprehensive recovery mechanism. Experimental results and analyses of existing methods reveal the framework's ability to balance high-security standards with optimal system performance, addressing key challenges such as zero-day attacks, partial encryption risks, and recovery downtimes. This multi-layered defense strategy ensures that big data systems can maintain their critical operations while mitigating the growing threat of ransomware. The proposed solution paves the way for further enhancements in securing large-scale, distributed data environments)
Analyzing Political Bias in Indian News Articles using NLP and Deep Learning
Chandrasekaran K, Usha Divakarla, Jeet Nilesh Desai
Proceedings of the 2025 International Conference on Artificial Intelligence and Emerging Technology Global AI Summit 2025, 2025
Concerns about fairness in journalism have grown with the rise of politically biased content, especially in countries like India. This study introduces a novel sentiment-informed clustering framework that scales political bias scores across six levels for Indian news, enabling more fine-grained bias detection than existing binary or ternary approaches. A custom-labeled dataset is created and transformed it into a multi-class classification task. We evaluated traditional machine learning models (Logistic Regression, SVM, Naïve Bayes, Random Forest) and deep learning models (BERT, RoBERTa, BiLSTM with RoBERTa embeddings). BiLSTM with RoBERTa embeddings achieved the highest accuracy of 96.91%. The study demonstrates a reliable approach for detecting political bias in news articles, contributing to efforts toward more transparent and balanced journalism.
A Sustainable Model of Security Automation: Approach and Analysis
Chandrasekaran K, Aditya Rathod, Usha Divakarla
2025 IEEE International Conference on Recent Advances in Computing and Systems Reacs 2025, 2025
Cybersecurity defenses must evolve as threats grow more complex and compliance requirements tighten. Traditional rule-based IDS and SIEM platforms often incur high false-positive rates and energy costs [1], [2], making long-term operation unsustainable. We propose an integrated security automation framework that embeds sustainability at its core. Our model combines AI-driven anomaly detection [2] for real-time threat identification, federated learning for privacy-preserving intelligence sharing [3], blockchain-based tamper-evident logging [4], and post-quantum cryptography to future-proof communications [6]. The architecture automates compliance with standards (e.g. NIST CSF, ISO/IEC 27001) and optimizes processing to minimize resource usage. Preliminary case studies in finance and healthcare demonstrate that this approach maintains high detection accuracy while significantly reducing false positives and energy consumption [5]. Overall, our sustainable security automation model enhances resilience and transparency without sacrificing efficiency [1], [5].
USING JIFF FOR COLLABORATIVE MEDICAL DATA ANALYSIS WITH SECURE MULTIPARTY COMPUTATION
Usha Divakarla, K Chandrasekaran, K Hemanth Kumar Reddy
Proceedings on Engineering Sciences, 2025
In collaborative data analysis, secure multiparty computation can be utilised to compute statistical functions in a private manner. For the purpose of developing apps that require safe multi-party collaboration, JIFF is an open-source JavaScript library. We use JIFF to create an application that allows two parties to collaborate on medical data analysis, including the processing of sensitive patient data while maintaining data confidentiality and privacy. A decentralized system that ensures data security and secure computation of private information is provided by the JIFF framework. We assess the system's functionality and privacy-preserving skills, proving that it can effectively protect data privacy while still producing reliable analysis findings.
Secure Intelligence Development Lifecycle (SIDL) Model for Vulnerability Detection
V. Dhanush, T. Sarath Chandra, Usha Divakarla, K. Chandrasekaran
Communications in Computer and Information Science, 2025
Skin Cancer Detection using Deep Learning
Usha Divakarla, Nikhil R Chandan, Poorva S P, Ranjan Shettigar, Pratham G Nayak
2025 International Conference on Artificial Intelligence and Data Engineering Aide 2025 Proceedings, 2025
Skin cancer becomes one of the various cancers most frequently diagnosed in most people around the globe; early diagnosis has always led to successful treatment. Deep learning techniques have shown to be useful for automated skin cancer detection from dermoscopic images. Here, we present a whole approach involving deep learning and machine learning features that can be used accurately for the efficient detection of skin cancer. We would work on building a highly functional deep learning model. The model would be trained on a large dataset of dermoscopic images that would feature various forms of skin lesions. Deep learning models, based on CNN technology, can capture minor features that present in an image; this therefore makes differentiation between malignant and benign lesions easier.
Identification of Hair and Scalp Diseases by Machine Learning Based Analysis
Usha Divakarla, Manish Shetty, Mohan Krishna, Mohith Gowda, Pratham Shetty
2025 International Conference on Artificial Intelligence and Data Engineering Aide 2025 Proceedings, 2025
Impact analysis of online education development and implementation using machine learning model
Usha Divakarla, K. Chandrasekaran
Virtual Lifelong Learning Educating Society with Modern Communication Technologies, 2024
Implementing Service-Oriented Game-Theoretic Security Scheme for IoV Networks in Self-driving Cars
Usha Divakarla, K. Chandrasekaran
Lecture Notes in Networks and Systems, 2024
Performance Evaluation of Botnet Attack Detection Using XAI
Usha Divakarla, K Chandrasekaran
2024 IEEE Region 10 Symposium Tensymp 2024, 2024
D-DNS: A Decentralized Domain Name System on the Blockchain: Implementation and Assessment
Usha Divakarla, K Chandrasekaran
2024 IEEE International Conference on Blockchain and Distributed Systems Security Icbds 2024, 2024
A Comprehensive Study on Role of Environmental Factors on Sericulture and Silkworm Disease Detection Techniques
Sandeep Kumar S, Usha Divakarla
4th IEEE International Conference on Mobile Networks and Wireless Communications Icmnwc 2024, 2024
A Stacked Model Approach for Machine Learning-Based Traffic Prediction
Usha Divakarla, K. Chandrasekaran
Lecture Notes in Networks and Systems, 2024
Enhancing Data Quality in Hybrid Cloud Architectures
Gerald Harry Fernandes, Usha Divakarla, Chandrasekaran K
Proceedings of the 4th International Conference on Ubiquitous Computing and Intelligent Information Systems Icuis 2024, 2024
A Systematic Literature Review on Multimodal Aspect-Based Sentiment Analysis
Usha Divakarla, K.V. Hitesh Krishna, Harshaditya Sharma, K Chandrasekarana
International Conference on Parallel Distributed and Grid Computing Pdgc, 2024
Smart Strategies for Energy-Aware Rouge Node Detection in Mobile Ad-hoc Networks
Usha Divakarala, Manjula Gururaj Rao, K Hemant Kumar Reddy
International Conference on Distributed Computing and Optimization Techniques Icdcot 2024, 2024
Advancements in Automated Livestock Monitoring: A Concise Review of Deep Learning-Based Cattle Activity Recognition
Deepak D, Demian Antony D'Mello, Usha Divakarla
10th International Conference on Advanced Computing and Communication Systems Icaccs 2024, 2024
Multimodal Approach to Decoding Facial Expressions Using LBPH and Deep Learning Techniques
Manjula Gururaj Rao, Usha Divakarala, Sushma K Bhat, Sohini Misra
IEEE International Conference on Recent Advances in Science and Engineering Technology Icraset 2024, 2024
Malware Classification Using XGBoost and Genetic Algorithm for Hyperparameter Tuning
Usha Divakarla, K Chandrasekaran, Harish S V, Pooja Gayatri Kanal, Shalini C
8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions Csitss 2024, 2024
A hierarchical blockchain architecture for secure data sharing for vehicular networks
Kripakar Srinivasan C, Usha Divakarala, K Chandrasekaran, K. Hemant Kumar Reddy
International Journal of Information Technology Singapore, 2023
Predicting Phishing Emails and Websites to Fight Cybersecurity Threats Using Machine Learning Algorithms
Usha Divakarla, K Chandrasekaran
2023 3rd International Conference on Smart Generation Computing Communication and Networking Smart Gencon 2023, 2023
Mulberry Leaves Diseases and Disease Identification Techniques
S Sandeep Kumar, Usha Divakarla
International Conference on Integrated Intelligence and Communication Systems Iciics 2023, 2023
Automation with Blockchain: Creating a Marketplace for IoT Based Irrigation System
Usha Divakarla, K. Chandrasekaran
Communications in Computer and Information Science, 2023
IOT Devices Using Supervised Machine Learning Models for Anomaly Based Intrusion Detection
Usha Divakarla, Chandrasekaran. K
2023 International Conference on Emerging Smart Computing and Informatics Esci 2023, 2023
DNS Cache Poisoning: Investigating Server and Client-Side Attacks and Mitigation Methods
K. Chandrasekaran, Usha Divakarla, C. K. Srinivasan
2023 Cyber Research Conference Ireland Cyber Rci 2023, 2023
Comprehensive Prediction Model for Player Selection in FIFA Manager Mode
Usha Divakarla, K. Chandrasekaran, K. Hemanth Kumar Reddy, Manjula Gururaj
Lecture Notes in Networks and Systems, 2023
A Multimodal Approach Utilizing the IOMT to Address both the Pandemic and its Aftermath
Manjula Gururaj Rao, Usha Divakarala, K Hemant Kumar Reddy, Priyanka H, Ch Sree Kumar
International Conference on Recent Advances in Science and Engineering Technology Icraset 2023, 2023
Semantic Segmentation for Autonomous Driving
Usha Divakarla, Ramyashree Bhat, Suraj B. Madagaonkar, D. V. Pranav, Chaithanya Shyam, K. Chandrashekar
Lecture Notes in Networks and Systems, 2023
A Machine Learning Approach for Load Balancing in a Multi-cloud Environment
Usha Divakarla, K. Chandrasekaran
Lecture Notes in Networks and Systems, 2022
A Novel Approach towards Windows Malware Detection System Using Deep Neural Networks
Usha Divakarla, K Hemant Kumar Reddy, K Chandrasekaran
Procedia Computer Science, 2022
A Deep Learning Approach Towards Building Intelligent Transport System
Manjula Gururaj Rao, H Priyanka, Ab Pradeep Kumar, Usha Divakarala
2022 2nd International Conference on Computer Science Engineering and Applications Iccsea 2022, 2022
Novel approach of Using Periocular and Iris Biometric Recognition in the Authentication of ITS
Manjula Gururaj Rao, Priyanka H, Sumathi Pawar, K Hemant Kumar Reddy, Usha Divakarla
2022 4th International Conference on Cognitive Computing and Information Processing Ccip 2022, 2022
Fusion Biometric Deep Features Blended in Its Authentication
Manjula Gururaj Rao, Sumathi Pawar, H Priyanka, K Hemant Kumar Reddy, Usha Divakarala
2nd IEEE International Conference on Advanced Technologies in Intelligent Control Environment Computing and Communication Engineering Icatiece 2022, 2022
A Machine Learning Approach for Daily Temperature Prediction Using Big Data
Usha Divakarla, K. Chandrasekaran, K. Hemanth Kumar Reddy, R. Vikram Reddy, Manjula Gururaj
Lecture Notes in Networks and Systems, 2022
Fully Decentralized Blockchain and Browser-Based Volunteer Computing Platform
K. S. Sagar Bharadwaj, Samvid Dharanikota, Adarsh Honawad, D. Usha, K. Chandrasekaran
Lecture Notes in Electrical Engineering, 2022
Workload classification in multi-vm cloud environment using deep neural network model
Paras Bhagtya, S Raghavan, K Chandraseakran, Usha D.
Proceedings of the ACM Symposium on Applied Computing, 2021
Towards a Federated Learning Approach for NLP Applications
Omkar Srinivas Prabhu, Praveen Kumar Gupta, P. Shashank, K. Chandrasekaran, D. Usha
Lecture Notes in Electrical Engineering, 2021
Optimized diet plan using unbounded knapsack Algorithm
Pramod Bobade, Prashant Kumar, K. Chandrasekaran, D. Usha
2020 IEEE International Conference on Computing Power and Communication Technologies Gucon 2020, 2020
Role of Activation Functions and Order of Input Sequences in Question Answering
B. S. Chenna Keshava, P. K. Sumukha, K. Chandrasekaran, D. Usha
Advances in Intelligent Systems and Computing, 2020
Enhanced Trust Path Between Two Entities in Cloud Computing Environment
Usha Divakarla, K. Chandrasekaran
Better Security and Encryption within Cloud Computing Systems, 2019
End-to-end monitoring of cloud resources using trust
D. Usha, K. Chandrasekaran
Advances in Intelligent Systems and Computing, 2019
A novel approach for evaluating trust of resources in cloud environment
Usha Divakarla, K. Chandrasekaran
IEEE Region 10 Annual International Conference Proceedings TENCON, 2017
Trusted path between two entities in Cloud
Usha Divakarla, Chandrasekaran K
Proceedings of the 2016 6th International Conference Cloud System and Big Data Engineering Confluence 2016, 2016
Trust models in cloud: A survey on pros and cons
Usha Divakarla, K. Chandra Sekaran
Lecture Notes in Electrical Engineering, 2015
An overview of cloud computing in distributed systems
Usha Divakarla, Geetha Kumari, R. B. Patel, B. P. Singh
Aip Conference Proceedings, 2010

RECENT SCHOLAR PUBLICATIONS

Detecting Lung Anomalies Through Transfer Learning-Based Deep Learning Based X-Ray and MRI Image Fusion
U Divakarla, HC Varshini, S Yukti
2026 Contemporary Computing Innovations Conference (CCIC), 1-6 , 2026
2026
RaagaDhvani: A novel augmented multi-feature dataset: Advancing emotion recognition in Carnatic music with multimodal features and hybrid deep learning
A Priyadarshini, U Divakarla
Data in Brief 64, 112364 , 2026
2026
Citations: 1
Utilizing Artificial Intelligence for the Identification of Plant Species and Detection of Diseases through Deep Learning
U Divakarla, K Chandrasekaran, S Govindarajan, S Sangameswaran
Decision Sciences in Bioinformatics, 168-187 , 2026
2026
A Sustainable Model of Security Automation: Approach and Analysis
K Chandrasekaran, A Rathod, U Divakarla
2025 IEEE International Conference on Recent Advances in Computing and … , 2025
2025
Analyzing Political Bias in Indian News Articles using NLP and Deep Learning
K Chandrasekaran, U Divakarla, JN Desai
2025 2nd Global AI Summit-International Conference on Artificial … , 2025
2025
Blockchain-Based Paddy Crop Marketplace: Enhancing Farmer-Buyer Trust Through Smart Contracts
A Kandasamy, K Chandrasekaran, U Divakarla, M Krishna
MMFood’25, 11 , 2025
2025
A Comprehensive Framework for Safeguarding Big Data Applications Against Ransomware Attacks
U Divakarla, K Chandrasekaran
2025 IEEE International Conference for Women in Innovation, Technology … , 2025
2025
Leveraging Machine Learning Algorithms to Scrutinize Code Structures for Security Weaknesses
U Divakarla, K Chandrasekaran
Integrating Advanced Technologies for Enhanced Security and Efficiency, 347-356 , 2025
2025
Identification of Hair and Scalp Diseases by Machine Learning Based Analysis
U Divakarla, M Shetty, M Krishna, M Gowda, P Shetty
2025 International Conference on Artificial Intelligence and Data … , 2025
2025
Citations: 1
Skin Cancer Detection using Deep Learning
U Divakarla, NR Chandan, R Shettigar, PG Nayak
2025 International Conference on Artificial Intelligence and Data … , 2025
2025
Stride-Based Threat Modeling for Blockchain Based Healthcare Supply Chain Management System
SV Harish, K Chandrasekaran, U Divakarla, V Ramana
Blockchain-Enabled Internet of Things Applications in Healthcare: Current … , 2025
2025
Citations: 1
USING JIFF FOR COLLABORATIVE MEDICAL DATA ANALYSIS WITH SECURE MULTIPARTY COMPUTATION
Divakarla U. , Chandrasekaran K. , Reddy K.H.K.
Proceedings on Engineering Sciences 7 (1), 669-678 , 2025
2025
A Systematic Literature Review on Multimodal Aspect-Based Sentiment Analysis
U Divakarla, KVH Krishna, H Sharma, K Chandrasekarana
2024 Eighth International Conference on Parallel, Distributed and Grid … , 2024
2024
Secure Intelligence Development Lifecycle (SIDL) Model for Vulnerability Detection
V Dhanush, TS Chandra, U Divakarla, K Chandrasekaran
International Conference on Advanced Network Technologies and Intelligent … , 2024
2024
Citations: 2
Enhancing Data Quality in Hybrid Cloud Architectures
GH Fernandes, U Divakarla
2024 4th International Conference on Ubiquitous Computing and Intelligent … , 2024
2024
A Comprehensive Study on Role of Environmental Factors on Sericulture and Silkworm Disease Detection Techniques
S Kumar, U Divakarla
2024 4th International Conference on Mobile Networks and Wireless … , 2024
2024
Citations: 7
Multimodal Approach to Decoding Facial Expressions Using LBPH and Deep Learning Techniques
MG Rao, U Divakarala, SK Bhat, S Misra
2024 International Conference on Recent Advances in Science and Engineering … , 2024
2024
Citations: 2
Malware Classification Using XGBoost and Genetic Algorithm for Hyperparameter Tuning
U Divakarla, K Chandrasekaran, PG Kanal
2024 8th International Conference on Computational System and Information … , 2024
2024
Citations: 1
D-dns: A decentralized domain name system on the blockchain: Implementation and assessment
U Divakarla, K Chandrasekaran
2024 IEEE International Conference on Blockchain and Distributed Systems … , 2024
2024
Citations: 4
Performance Evaluation of Botnet Attack Detection Using XAI
U Divakarla, K Chandrasekaran
2024 IEEE Region 10 Symposium (TENSYMP), 1-7 , 2024
2024

MOST CITED SCHOLAR PUBLICATIONS

A hierarchical blockchain architecture for secure data sharing for vehicular networks
Kripakar Srinivasan C, Usha Divakarala, K Chandrasekaran, K. Hemant Kumar Reddy
International Journal of Information Technology 15 (doi.org/10.1007/s41870 … , 2023
2023
Citations: 23
A novel approach towards windows malware detection system using deep neural networks
U Divakarla, KHK Reddy, K Chandrasekaran
Procedia Computer Science 215, 148-157 , 2022
2022
Citations: 23
Load balancing of virtual machine resources in cloud using genetic algorithm
K Chandrasekaran, U Divakarla
ICCN conference at national institute of technology karnataka, surathkal … , 2013
2013
Citations: 21
An overview of cloud computing in distributed systems
U Divakarla, G Kumari
AIP conference proceedings 1324 (1), 184-186 , 2010
2010
Citations: 16
Workload Classification in Multi-VM Cloud Environment using Deep Neural Network Model
Usha D, Paras B, Raghavan S, K Chandrasekaran
SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing … , 2021
2021
Citations: 12
A Comprehensive Study on Role of Environmental Factors on Sericulture and Silkworm Disease Detection Techniques
S Kumar, U Divakarla
2024 4th International Conference on Mobile Networks and Wireless … , 2024
2024
Citations: 7
Predicting Phishing Emails and Websites to Fight Cybersecurity Threats Using Machine Learning Algorithms
KC Usha Divakarla
2023 3rd International Conference on Smart Generation Computing … , 2024
2024
Citations: 7
Semantic segmentation for autonomous driving
U Divakarla, R Bhat, SB Madagaonkar, DV Pranav, C Shyam, ...
Information and Communication Technology for Competitive Strategies (ICTCS … , 2023
2023
Citations: 7
A Deep Learning Approach Towards Building Intelligent Transport System
MG Rao, H Priyanka, AP Kumar, U Divakarala
2022 Second International Conference on Computer Science, Engineering and … , 2022
2022
Citations: 6
Towards a federated learning approach for nlp applications
OS Prabhu, PK Gupta, P Shashank, K Chandrasekaran, D Usha
Applications of Artificial Intelligence and Machine Learning: Select … , 2021
2021
Citations: 6
Enhanced Trust Path between Two Entities in Cloud Computing Environment
U Divakarla, K Chandrasekaran
International Journal of Cloud Application and Computing 6 (3), 15-31 , 2016
2016
Citations: 6
Mulberry leaves diseases and disease identification techniques
SS Kumar, U Divakarla
2023 International Conference on Integrated Intelligence and Communication … , 2023
2023
Citations: 5
A novel approach for evaluating trust of resources in cloud environment
U Divakarla, K Chandrasekaran
2016 IEEE Region 10 Conference (TENCON), 459-463 , 2016
2016
Citations: 5
Trusted path between two entities in Cloud
U Divakarla, K Chandrasekaran
2016 6th International Conference-Cloud System and Big Data Engineering … , 2016
2016
Citations: 5
D-dns: A decentralized domain name system on the blockchain: Implementation and assessment
U Divakarla, K Chandrasekaran
2024 IEEE International Conference on Blockchain and Distributed Systems … , 2024
2024
Citations: 4
Advancements in Automated Livestock Monitoring: A Concise Review of Deep Learning-Based Cattle Activity Recognition
D Deepak, DA D'Mello, U Divakarla
2024 10th International Conference on Advanced Computing and Communication … , 2024
2024
Citations: 4
Novel approach of Using Periocular and Iris Biometric Recognition in the Authentication of ITS
MG Rao, H Priyanka, S Pawar, KHK Reddy, U Divakarla
2022 Fourth International Conference on Cognitive Computing and Information … , 2022
2022
Citations: 4
Trust models in cloud: A survey on pros and cons
U Divakarla, KC Sekaran
New Trends in Networking, Computing, E-learning, Systems Sciences, and … , 2014
2014
Citations: 4
Optimized diet plan using unbounded knapsack Algorithm
P Bobade, P Kumar, K Chandrasekaran, D Usha
2020 IEEE International Conference on Computing, Power and Communication … , 2020
2020
Citations: 3
Logistic Regression based DFS for Trip Advising Software (ASCEND)
E Thomas, A Byju, K Chandrasekaran, D Usha
9th International Conference on Cloud Computing, Data Science & Engineering … , 2019
2019
Citations: 3

Usha Divakarla

RESEARCH, TEACHING, or OTHER INTERESTS

Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS