Dr Surabhi Saxena

@m.christuniversity.in

Assistant Professor , Department of Computer Science , Christ University , Bangalore
CHRIST ( Deemed to be University )

Dr Surabhi Saxena


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https://researchid.co/surabhisaxena
Surabhi Saxena received the Ph.D. degree from the Department of Computer Application, Babu Banarasi Das University, Lucknow, India, in 2021. She is currently an Assistant Professor with the Department of Computer Science and Engineering, CHRIST University, Central Campus, Bengaluru, India. She has more than five years of teaching experience and six years of research experience. She has one national patent. Her research has been recorded in over 20 journal publications and international conferences and five international conference reviewers. Her research and publication interests include artificial intelligence, machine learning, security software quality software, software engineering, and soft computing. She is also working in the areas of e-commerce, e-governance, hybrid data security system, voronoi partitioning, deep learning, data science, and the IoT. She is a Life Time Member of IAENG and IACSIT. She is also the Editor-in-Chief and an Editor of Blue Eyes Publications and a Soft

EDUCATION

Dr Surabhi Saxena received the Ph.D. degree from the Department of Computer Application, Babu Banarasi Das University, Lucknow, India, in 2021. She is currently an Assistant Professor with the Department of Computer Science and Engineering, CHRIST University, Central Campus, Bengaluru, India. She has more than five years of teaching experience and six years of research experience. She has one national patent. Her research has been recorded in over 20 journal publications and international conferences and five international conference reviewers

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence, Computer Science Applications, Information Systems
25

Scopus Publications

356

Scholar Citations

9

Scholar h-index

9

Scholar i10-index

Scopus Publications

  • Development of a VR-Based Solid Waste Management Awareness Platform Utilizing YOLOv12 and MSCNN
    Sambhav Jain, Suresh K, Neha Singhal, Surabhi Saxena
    2026 2nd International Conference on Cognitive Computing in Engineering Communications Sciences and Biomedical Health Informatics Ic3ecsbhi 2026, 2026
    Waste management is not an issue concerning an individual but a collective responsibility. It refers to our environment. Our project, “Solid Waste Management,” verifies the efficacy of virtual reality, a novel learning modality for the user, acquiring knowledge of waste segregation and the right way of waste disposal simulation through virtual reality. The implication of virtual reality's addition into the educational system was analyzed through the acceptance of the model and the acquisition of knowledge through the task performed by the user. The simulation and the environment of virtual reality are implemented through the use of Spatial Awareness, Haptic Simulation, Hand Tracking using HCI, and Immersive Learning Environment for a genuine simulation experience for the user. The simulation environment and software were developed using the Unity environment creating a gamified world using a 3D environment and the Blender software used for the development of the 3D models. The simulation environment's implementation into the various HMD devices also used the OpenXR plugin. The simulation is further segmented into two parts: interior and exterior waste management. This novel simulation technique will not only enable the acquisition of the most requisite skills of waste segregation but also the acquisition of environment knowledge and the promotion of people towards sustainable practices
  • An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining
    Charan Kumar Ala, Zefree Lazarus Mayaluri, Aman Kaushik, Nikhat Parveen, Surabhi Saxena, Abu Taha Zamani, Debendra Muduli
    Results in Engineering, 2025
    Blast induced ground vibrations (BIGV) pose critical challenges in surface mining , threatening structural integrity, worker safety, and environmental compliance. This study proposes a novel hybrid artificial intelligence (AI) framework that integrates physics informed neural networks (PINNs) with conventional machine learning (ML) algorithms for the accurate prediction and optimization of BIGV. Unlike empirical equations that lack generalizability or black box ML models with limited transparency, the proposed approach embeds domain specific physical laws while leveraging data driven learning to improve both predictive accuracy and interpretability. A multiobjective optimization scheme is employed to balance competing goals: minimizing peak particle velocity (PPV), maximizing fragmentation efficiency, and reducing operational costs. Crucially, the framework incorporates Explainable AI (XAI) techniques such as Shapley Additive Explanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME) and uncertainty quantification (UQ) methods based on Bayesian Neural Networks to provide insight into model decisions and confidence in predictions. Validation across five operational mines in the Godavari Valley Coalfields (India) demonstrates strong generalizability, achieving up to a 20% reduction in RMSE compared to empirical baselines. The improvement is statistically significant ( p < 0.01 ) as confirmed through a paired t-test across cross-validation folds. These findings highlight that a physics informed, explainable, and uncertainty aware AI framework can substantially improve vibration prediction, ensure regulatory compliance, and support safer, more sustainable blasting operations in modern surface mining.
  • ANFIS-Based Multi-Sensor Data Fusion Model for Optimized Autonomous Vehicle Navigation Using Big Data and Filtering Techniques
    Sanjay Kumar, Anand Ranjan, Surabhi Saxena, Ashish
    Mobile Networks and Applications, 2025
  • Visualization of Data Structures and Algorithms with Dynamic Memory Allocation
    Eileen Maria Tom, Neha Singhal, Surabhi Saxena
    2025 IEEE 7th International Conference on Computing Communication and Automation Iccca 2025, 2025
    Data Structures and Algorithms (DSA) is fundamental to computer science education, yet novice learners face significant challenges in grasping abstract concepts and their system-level implications, such as dynamic memory allocation. This paper presents a novel web-based platform designed to enhance learning outcomes for beginner to intermediate students through interactive step-by-step visualizations of DSA, including arrays, linked lists, stacks, queues, and searching and sorting algorithms. A distinctive feature is the integration of dynamic memory allocation visualization, illustrating stack and heap to elucidate system-level operations. Developed using Next.js, Tailwind CSS, D3.js, and Framer Motion, the platform offers a space-themed responsive interface with synchronized code, data structure, and memory views. By addressing pedagogical gaps in tools like VisuAlgo, this work aligns with Sustainable Development Goal 4- Quality Education, promoting accessible and equitable learning.
  • Evaluate Machine Learning Techniques for Early Disease of Cardiovascular Disease
    M T Sushmitha, Surabhi Saxena, Neha Singhal
    5th IEEE International Conference on Mobile Networks and Wireless Communications Icmnwc 2025, 2025
    Cardiovascular diseases are one of the major causes of death around the world, and their early detection is critical for effective intervention. The paper presents a systematic review of machine learning techniques used for the early prediction of cardiovascular diseases, focusing on studies carried out between 2019 and 2024. Widely used models considered in the review include Logistic Regression, Support Vector Machines, Random Forest, K-Nearest Neighbors, Gradient Boosting, and hybrid ensemble methods with the aim of ascertaining predictive accuracy, interpretability, and clinical relevance. In most of the reviewed studies, ensemble and Random Forest models attained the highest accuracies of $\mathbf{9 0 \% - 9 8 \%}$, while Gradient Boosting and SVMs were mostly above 90% in balanced datasets. Logistic Regression had a moderate accuracy of $85 \%-91 \%$ but remained the most interpretable, while KNN established the lowest performance of $80 \%-86 \%$. Despite the promising strides, there are a number of limitations, such as imbalance in datasets, limited external validation, and small benchmark datasets, that are limiting general application in health. This systematic review highlights strengths and weaknesses of the contemporary machine learning approaches and makes it evident that clinically validated, interpretable, and generalizable models should be developed in order to assist real-world medical decision-making.
  • Latency Reduction and Input Prediction for Cloud Gaming Clients
    Gavin George Payankan, Neha Singal, Surabhi Saxena
    Proceedings 2025 International Conference on Transformative Computing Technologies Ictct 2025, 2025
    Cloud gaming enables access to high-quality games on thin clients by streaming rendered content from remote servers, but network-induced latency remains a critical barrier to responsive gameplay. This paper presents a browser-based system that profiles user input in real-time, employs a lightweight machine learning model to predict actions, and dynamically compensates for lag by speculative input. Our solution reduces perceived lag by up to 25% and maintains a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$94 {\%}+$</tex> prediction accuracy, fully within a free-tier cloud environment. Compared to traditional infrastructure-based approaches, our method imposes no proprietary hardware requirements and offers platform-wide scalability.
  • Comparative Analysis of Machine Learning Algorithms for Effective Crop Recommendation
    Gamya K, Surabhi Saxena, Neha Singhal
    1st IEEE International Conference on Data Science and Intelligent Network Computing Icdsinc 2025, 2025
    The global call for sustainable farming necessitates a move away from traditional crop selection methods. These conventional approaches, often relying on farmer intuition, are imprecise and scale poorly in the face of complex environmental variables. Machine Learning (ML) models offer a robust, datadriven solution. By analyzing multifaceted data-spanning soil chemistry, weather patterns, precipitation trends, and historical yield performance-ML models can significantly enhance decision-making, optimize resource utilization, and improve overall crop outcomes. This paper delivers an extensive comparative review of key ML algorithms employed for crop recommendation, including Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN). We also explore the critical role of Explainable AI (XAI) in building model transparency. Our study evaluates these models on the metrics of accuracy, interpretability, and computational overhead. The research also investigates hybrid methods that integrate deep learning with conventional ML to enhance predictive power. Our comparative findings highlight the strengths and weaknesses of each model, concluding that ANN and XAI-based approaches demonstrate the highest accuracy and adaptability for diverse agricultural conditions. We also identify significant challenges, such as data imbalances and the absence of real-time data, and discuss future trends like the integration of IoT, remote sensing, and federated learning, which will be key to making precision farming scalable and accessible.
  • An Explainable AI Techniques for Advancing Diabetes Prediction Using Machine Learning
    Surabhi Saxena, Nikhat Parveen, Bhanu Naga Karthik Yarlagadda, Appasani Siva Siva Srujan, Mannava Ashok Kumar, Koka Likhitha
    2025 International Conference on Intelligent Control Computing and Communications Ic3 2025, 2025
    Researchers have developed an automated system to identify diabetes risk. This system combines data from two sources: a collection of female patients in Bangladesh and an expanded dataset from a local textile factory. The expanded dataset includes information from 203 additional patients. The system uses several techniques to improve its accuracy. It first identifies the most important factors for predicting diabetes, then employs a special model to estimate insulin levels. It also addresses challenges like imbalanced data (where one outcome is more common) and explains its predictions using artificial intelligence techniques. This system achieved the superlative results has an 81.0% accuracy rate, 0.812 F1 score, and 0.844 Area Under the Curve (AUC).. These metrics indicate strong performance in identifying diabetes risk.
  • SIDNet: A SQL Injection Detection Network for Enhancing Cybersecurity
    Debendra Muduli, Shantanu Shookdeb, Abu Taha Zamani, Surabhi Saxena, Anuradha Shantanu Kanade, Nikhat Parveen, Mohammad Shameem
    IEEE Access, 2024
    SQL (Structured Query Language) injection is one of the most prevalent and dangerous forms of cyber-attacks, posing significant threats to database management systems and the overall security of web applications. By exploiting vulnerabilities in web applications, attackers can execute malicious SQL statements, potentially compromising the integrity and confidentiality of critical data. To combat these threats, in this study, we introduce two novel CNN models, SIDNet-1 (SQL Injection-attack Detection Network-1) and SIDNet-2 (SQL Injection-attack Detection Network-2), specifically designed for the classification of SQL injection attacks to bolster web application security. Our comprehensive evaluation includes a comparison of the performance of these customized CNN models against traditional machine learning approaches, highlighting improvements in classification accuracy and reductions in false alarm rates. The proposed models have been experimented with two publicly available dataset SQLI (SQL-Injection) and SQLV2 (SQL-Injection version2). Specifically, SIDNet-1 achieves an impressive accuracy of 98.02% on the SQLI dataset, while SIDNet-2 closely follows with 97.54%. Furthermore, on the SQLIV2 dataset, SIDNet-1 attains 97.77%, and SIDNet-2 achieves 97.83% accuracy respectively.
  • Heterogeneous data-based information retrieval using a fine-tuned pre-trained BERT language model
    Amjan Shaik, Surabhi Saxena, Manisha Gupta, Nikhat Parveen
    Multimedia Tools and Applications, 2024
  • Transforming transportation: Embracing the potential of 5G, heterogeneous networks, and software defined networking in intelligent transportation systems
    Surabhi Saxena, Radha Raman Chandan, Ramkumar Krishnamoorthy, Upendra Kumar, Prabhdeep Singh, Ashish Kumar Pandey, Shashi Kant Gupta
    Journal of Autonomous Intelligence, 2024
  • Brain Tumor Classification Using an Ensemble of Deep Learning Techniques
    S Gopal Krishna Patro, Nikhil Govil, Surabhi Saxena, Brojo Kishore Mishra, Abu Taha Zamani, Achraf Ben Miled, Nikhat Parveen, Hashim Elshafie, Mosab Hamdan
    IEEE Access, 2024
  • Medical Image Analysis Using Deep Learning Technnique
    Surabhi Saxena, Nikhat Parveen, Devendra Agarwal
    ACM International Conference Proceeding Series, 2023
  • Project-based learning: Design of data acquisition module for greenhouse system
    Debashish Mohapatra, Subhransu Padhee, Surabhi Saxena, Bhaskar Patnaik
    International Journal of Electrical Engineering and Education, 2023
  • Machine learning algorithms for diabetes detection: a comparative evaluation of performance of algorithms
    Surabhi Saxena, Debashish Mohapatra, Subhransu Padhee, Goutam Kumar Sahoo
    Evolutionary Intelligence, 2023
  • Real Estate Property Price Estimator Using Machine Learning
    Disha UB, Surabhi Saxena
    Proceedings of International Conference on Computational Intelligence and Sustainable Engineering Solution Cises 2023, 2023
  • Online Crop Doctor using Machine Learning and Deep Learning
    Narayana P, Surabhi Saxena
    Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing Icaaic 2023, 2023
  • Detection of Number Plate in Vehicles using Deep Learning based Image Labeler Model
    Shashi Kant Gupta, Surabhi Saxena, Alex Khang, Bramah Hazela, Chandra Kumar Dixit, Bhadrappa Haralayya
    Icrtec 2023 Proceedings IEEE International Conference on Recent Trends in Electronics and Communication Upcoming Technologies for Smart Systems, 2023
  • A Prediction of Disease Using Machine Learning Approach
    Surabhi Saxena, Nupur Soni, Akhilesh Kumar, Anshul Mishra
    Eai Springer Innovations in Communication and Computing, 2022
  • Electronic Copy Technologies for IoT Machinery
    Armstrong Joseph J, Gaurav Dhiman, Surabhi Saxena, Sachin K. Korde, Narinder Kumar Bhasin
    Proceedings of the 2022 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2022, 2022
  • Deployment of Autonomous Vehicles in Agricultural and using Voronoi Partitioning
    Shailendra Tahilyani, Surabhi Saxena, Dimitrios A. Karras, Shashi Kant Gupta, Chandra Kumar Dixit, Bhadrappa Haralayya
    IEEE International Conference on Knowledge Engineering and Communication Systems Ickes 2022, 2022
  • A Novel Computation Offloading under 6G LEO Satellite-UAV-based IoT
    Kriti Jaiswal, Anshul Dahiya, Surabhi Saxena, Virendra Singh, Archita Singh, Arun Kushwaha
    Proceedings 2022 3rd International Conference on Computation Automation and Knowledge Management Iccakm 2022, 2022
  • Hybrid Cloud Computing for Data Security System
    Surabhi Saxena, Diwakar Yagyasen, Ch Naga Saranya, Raja Sarath Kumar Boddu, Amit Kumar Sharma, Shashi Kant Gupta
    2021 International Conference on Advancements in Electrical Electronics Communication Computing and Automation Icaeca 2021, 2021
  • Towards a fuzzy logic rule based prediction for effective adoption of E-procurement system on cloud environment
    , Surabhi Saxena, Dr. Devendra Agarwal, and
    International Journal of Engineering and Advanced Technology, 2019
  • Model to Quantify the Correctness for Adoption of Effective E-Procurement Process
    Surabhi Saxena, Devendra Agarwal
    2018 International Conference on Computational and Characterization Techniques in Engineering and Sciences Cctes 2018, 2018

RECENT SCHOLAR PUBLICATIONS

  • ANFIS-Based Multi-Sensor Data Fusion Model for Optimized Autonomous Vehicle Navigation Using Big Data and Filtering Techniques
    S Kumar, A Ranjan, S Saxena, Ashish
    Mobile Networks and Applications, 1-15 , 2026
    2026
  • Development of a VR-Based Solid Waste Management Awareness Platform Utilizing YOLOv12 and MSCNN
    S Jain, K Suresh, N Singhal, S Saxena
    2026 2nd International Conference on Cognitive Computing in Engineering … , 2026
    2026
  • Computer peripheral device with display screen
    DMUK Mohammad Zunnun Khan, DR SURABHI SAXENA, Dr. Sangeeta Mishra
    2026
  • Evaluate Machine Learning Techniques for Early Disease of Cardiovascular Disease
    MT Sushmitha, S Saxena, N Singhal
    2025 5th International Conference on Mobile Networks and Wireless … , 2025
    2025
  • Visualization of Data Structures and Algorithms with Dynamic Memory Allocation
    EM Tom, N Singhal, S Saxena
    2025 IEEE 7th International Conference on Computing, Communication and … , 2025
    2025
  • Latency Reduction and Input Prediction for Cloud Gaming Clients
    GG Payankan, N Singal, S Saxena
    2025 International Conference on Transformative Computing Technologies … , 2025
    2025
  • An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining
    CK Ala, ZL Mayaluri, A Kaushik, N Parveen, S Saxena, AT Zamani, ...
    Results in Engineering 27, 106046 , 2025
    2025
    Citations: 6
  • Particle swarm optimization approach to heterogeneous function
    T Niharika, SS Mehboob, KDM Suchindhra, K Guntapalli, N Parveen, ...
    Recent Advances in Sciences, Engineering, Information Technology … , 2025
    2025
  • An Explainable AI Techniques for Advancing Diabetes Prediction Using Machine Learning
    S Saxena, N Parveen, BNK Yarlagadda, ASS Srujan, MA Kumar, ...
    2025 International Conference on Intelligent Control, Computing and … , 2025
    2025
    Citations: 2
  • SIDNet: A SQL injection detection network for enhancing cybersecurity
    D Muduli, S Shookdeb, AT Zamani, S Saxena, AS Kanade, N Parveen, ...
    Ieee Access 12, 176511-176526 , 2024
    2024
    Citations: 10
  • Brain Tumor Classification Using an Ensemble of Deep Learning Techniques
    SGK Patro, N Govil, S Saxena, BK Mishra, AT Zamani, AB Miled, ...
    IEEE Access 12 , 2024
    2024
    Citations: 32
  • Heterogeneous data-based information retrieval using a fine-tuned pre-trained BERT language model
    A Shaik, S Saxena, M Gupta, N Parveen
    Multimedia Tools and Applications 83 (21), 59537-59559 , 2024
    2024
    Citations: 3
  • Transforming transportation: Embracing the potential of 5G, heterogeneous networks, and software defined networking in intelligent transportation systems
    S Saxena, RR Chandan, R Krishnamoorthy, U Kumar, P Singh, ...
    Journal of Autonomous Intelligence 7 (4) , 2024
    2024
    Citations: 20
  • Medical image analysis using deep learning technnique
    S Saxena, N Parveen, D Agarwal
    Proceedings of the 5th International Conference on Information Management … , 2023
    2023
    Citations: 2
  • Online Crop Doctor using Machine Learning and Deep Learning
    P Narayana, S Saxena
    2023 2nd International Conference on Applied Artificial Intelligence and … , 2023
    2023
    Citations: 5
  • Real estate property price estimator using machine learning
    UB Disha, S Saxena
    2023 International Conference on Computational Intelligence and Sustainable … , 2023
    2023
    Citations: 4
  • Security in IoT layers: Emerging challenges with countermeasures
    SA Ansar, S Arya, S Aggrawal, S Saxena, A Kushwaha, PC Pathak
    Computer vision and robotics: Proceedings of CVR 2022, 551-563 , 2023
    2023
    Citations: 22
  • Machine learning algorithms for diabetes detection: a comparative evaluation of performance of algorithms
    S Saxena, D Mohapatra, S Padhee, GK Sahoo
    Evolutionary intelligence 16 (2), 587-603 , 2023
    2023
    Citations: 42
  • Project-based learning: Design of data acquisition module for greenhouse system
    D Mohapatra, S Padhee, S Saxena, B Patnaik
    International Journal of Electrical Engineering & Education 60 (2), 168-187 , 2023
    2023
    Citations: 7
  • Detection of number plate in vehicles using deep learning based image labeler model
    SK Gupta, S Saxena, A Khang, B Hazela, CK Dixit, B Haralayya
    2023 International Conference on Recent Trends in Electronics and … , 2023
    2023
    Citations: 18

MOST CITED SCHOLAR PUBLICATIONS

  • Hybrid cloud computing for data security system
    S Saxena, D Yagyasen, CN Saranya, RSK Boddu, AK Sharma, SK Gupta
    2021 International Conference on Advancements in Electrical, Electronics … , 2021
    2021
    Citations: 94
  • Machine learning algorithms for diabetes detection: a comparative evaluation of performance of algorithms
    S Saxena, D Mohapatra, S Padhee, GK Sahoo
    Evolutionary intelligence 16 (2), 587-603 , 2023
    2023
    Citations: 42
  • Brain Tumor Classification Using an Ensemble of Deep Learning Techniques
    SGK Patro, N Govil, S Saxena, BK Mishra, AT Zamani, AB Miled, ...
    IEEE Access 12 , 2024
    2024
    Citations: 32
  • Deployment of autonomous vehicles in agricultural and using Voronoi partitioning
    S Tahilyani, S Saxena, DA Karras, SK Gupta, CK Dixit, B Haralayya
    2022 International Conference on Knowledge Engineering and Communication … , 2022
    2022
    Citations: 27
  • Security in IoT layers: Emerging challenges with countermeasures
    SA Ansar, S Arya, S Aggrawal, S Saxena, A Kushwaha, PC Pathak
    Computer vision and robotics: Proceedings of CVR 2022, 551-563 , 2023
    2023
    Citations: 22
  • Transforming transportation: Embracing the potential of 5G, heterogeneous networks, and software defined networking in intelligent transportation systems
    S Saxena, RR Chandan, R Krishnamoorthy, U Kumar, P Singh, ...
    Journal of Autonomous Intelligence 7 (4) , 2024
    2024
    Citations: 20
  • Detection of number plate in vehicles using deep learning based image labeler model
    SK Gupta, S Saxena, A Khang, B Hazela, CK Dixit, B Haralayya
    2023 International Conference on Recent Trends in Electronics and … , 2023
    2023
    Citations: 18
  • A novel computation offloading under 6G LEO satellite-UAV-based IoT
    K Jaiswal, A Dahiya, S Saxena, V Singh, A Singh, A Kushwaha
    2022 3rd International Conference on Computation, Automation and Knowledge … , 2022
    2022
    Citations: 11
  • SIDNet: A SQL injection detection network for enhancing cybersecurity
    D Muduli, S Shookdeb, AT Zamani, S Saxena, AS Kanade, N Parveen, ...
    Ieee Access 12, 176511-176526 , 2024
    2024
    Citations: 10
  • Confidentiality assessment model to estimate security during effective E-procurement process
    S Saxena, D Agarwal
    International Journal of Computer Sciences and Engineering (IJCSE) 6 (1 … , 2018
    2018
    Citations: 8
  • Project-based learning: Design of data acquisition module for greenhouse system
    D Mohapatra, S Padhee, S Saxena, B Patnaik
    International Journal of Electrical Engineering & Education 60 (2), 168-187 , 2023
    2023
    Citations: 7
  • An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining
    CK Ala, ZL Mayaluri, A Kaushik, N Parveen, S Saxena, AT Zamani, ...
    Results in Engineering 27, 106046 , 2025
    2025
    Citations: 6
  • Online Crop Doctor using Machine Learning and Deep Learning
    P Narayana, S Saxena
    2023 2nd International Conference on Applied Artificial Intelligence and … , 2023
    2023
    Citations: 5
  • Realiability Assessment Model to Estimate Quality of the Effective E-Procurement Process in Adoption
    S Saxena, D Agarwal
    International Journal of Scientific Research in Network Security and … , 2018
    2018
    Citations: 5
  • Model to quantify security for adoption of effective e-procurement process
    S Saxena, D Agarwal
    Journal of Emerging Technologies and Innovative Research (JETIR) 5 (5), 792-796 , 2018
    2018
    Citations: 5
  • A Systematic Literature Review on Software Reliability Estimation Model for Measuring the Effectiveness of Object Oriented Design
    S Saxena, DD Agarwal
    International Journal of Advanced Research in Computer and Communication … , 2017
    2017
    Citations: 5
  • Real estate property price estimator using machine learning
    UB Disha, S Saxena
    2023 International Conference on Computational Intelligence and Sustainable … , 2023
    2023
    Citations: 4
  • A modern approach to building a data science framework delivery pipeline using DevOps practices
    S Saxena, SK Gupta, S Poongodi, P Singh
    Turkish Journal of Computer and Mathematics Education 12 (11), 2507-2521 , 2021
    2021
    Citations: 4
  • Completeness assessment model to estimate quality of the effective e-procurement process in adoption
    S Saxena, D Agarwal
    International Journal of Management, IT and Engineering 8 (6), 369-380 , 2018
    2018
    Citations: 4
  • A Critical Literature Survey on Factors that Effecting E-Procurement Software
    S Saxena, D Agarwal
    International Journal of Advanced Research in Computer Engineering … , 2018
    2018
    Citations: 4