Ahirwar is working as an Associate Professor in the Department of Science & Technology under Faculty of Education & Methodology in Compucom Institute of Technology & Management, Jaipur (Rajasthan), India. She is having 20 years of experience in Teaching and Research. Her research areas are Medical Imaging, Data Mining and Celestial sound, IOT, Machine learning. She has published more than 50 research papers in repute National/International Journals and Conferences, authored and reviewed many books published by National and International publisher, published five patents in “Publication of the Patent Office”, Journal. She had delivered several Expert/Guest Lectures and Chaired Sessions in Various IEEE, International Conferences.
EDUCATION
Phd. (Computer Applications)
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science Applications
22
Scopus Publications
460
Scholar Citations
7
Scholar h-index
6
Scholar i10-index
Scopus Publications
Examining the Development of Cloud Kitchens: Consequences for the Hospitality and Tourism Industries’ Future Anamika Ahirwar Robotics in Hotel Services Housekeeping Reception and Concierge Services, 2026 Cloud kitchens are changing the nature of hospitality and tourism because they are a digitally enabled form of restaurant that requires shared infrastructure to operate. They are cost-effective, efficient, and scalable by eliminating the dine-in and relying on AI, IoT, and analytics. They open up virtual brands and small businesses as well. However, they are threatening the business of traditional restaurants because they are concerned with the quality of food, monotony, regulations, and the loss of cultural dining. In this chapter, the cloud kitchen and traditional kitchen models are compared based on sustainability, customer experience, and policy. Stakeholders, operations, and technology share a framework with each other. The findings highlight the point that cloud kitchens encourage innovation; however, the balance between convenience and experience and policy integration requires moderation to make the developments sustainable in the long run.
Empirical Approaches to Personality Prediction via Automated Handwriting Analysis: A Critical Review and Demonstration of Capabilities Anamika Ahirwar, Saurabh Bhargava, Dev Brat Gupta, Vinod Bhatt Leveraging AI for Future Ready Writing Education, 2026 This chapter will analyze the history of personality forecasting through handwriting exploration critically, since it focuses on a radical shift from traditional and subjective graphology to modern and objective graphology involving the application of machine learning and deep learning. Some of the methods used in the given field by the researchers include collecting data using complex systems, fine-tuning, handling it, feature extraction, as well as constructing complex deep learning systems. This study collects data based on the studies conducted in recent times and presents how automated systems are becoming more competent than the systems used before. In addition to this, it solves some grave problems that affect the interdisciplinary work, such as the imbalanced data, how the models operate, the computational requirements, ethical issues, and the challenges faced in applying the technologies in offices. The chapter also identifies the possibilities of future studies that emphasize the importance of powerful, equitable, and beneficial diagnostic technologies.
Triboinformatic analysis and prediction of B4C and granite powder filled Al 6082 composites using machine learning regression models Amit Aherwar, Anamika Ahirwar, Vimal Kumar Pathak Scientific Reports, 2025 The traditional methods for fabricating and evaluating wear properties are inherently time-consuming and financially demanding. To address these challenges, machine learning (ML) has emerged as a potent approach in predicting the mechanical and tribological behavior of advanced materials, including Al-based composites. The primary aim of this study is to combine experimental methodologies with ML algorithms to accurately predict the wear and coefficient of friction for B 4 C-granite composites, thereby aiding in the design and manufacturing of materials with enhanced wear performance. The composites were synthesized using stir casting, and wear behaviour was experimentally evaluated under dry sliding conditions using a pin-on-disc tribometer, resulting in a dataset comprising 81 samples. The experiments revealed that wear loss increased with higher load and lower reinforcement percentage, reaching to 0.315 g at 2.5%, 30 N, 1.67 m/s and 1200 m, compared to minimum wear loss of 0.029 g at 7.5%, 10 N, 0.83 m/s and 600 m. Seven different supervised regression-based ML models were applied to accurately predict wear characteristics, with hyperparameter tuning conducted to ensure a robust comparative analysis. The developed model’s results were evaluated utilizing a number of statistical metrics to identify the most reliable algorithm for wear and COF prediction. These models training and validation has been performed using experimental data, demonstrated strong potential for predicting tribological behavior with high accuracy, thereby reducing the need for extensive physical testing. Among all the approaches, the Fuzzy logic model achieved the highest predictive performance with highest R 2 of 0.9638 and lowest MAE of 0.0023 for wear loss and R 2 value of 0.9833 and lowest MAE of 0.0059 for COF, respectively. In addition, the Pearson coefficient correlation map establishes that reinforcement percentage have strong negative correlation of (− 0.57) and (− 0.50) with wear loss and COF.
Dry sliding tribological characteristics evaluation and prediction of TiB2-CDA/Al6061 hybrid composites exercising machine learning methods Amit Aherwar, Anamika Ahirwar, Vimal Kumar Pathak Scientific Reports, 2025 This study presents the fabrication and comprehensive tribological assessment of Al6061-based hybrid composites reinforced with Titanium diboride (TiB 2 ) and cow dung ash (CDA) using the stir casting technique. The wear behavior of TiB 2 -CDA/Al6061 composites was systematically analyzed under dry sliding conditions utilizing a pin-on-disc setup. The study investigates the effects of key parameters, including reinforcement percentage (R), applied load (L), sliding velocity (V), and sliding distance (D), on wear loss and the coefficient of friction (COF) through a full factorial experimental design. Additionally, scanning electron microscopy (SEM) was employed to examine dominant wear mechanisms under extreme wear conditions, revealing adhesion, abrasion, oxidation, and delamination as primary degradation processes. Furthermore, machine learning techniques, including Random Forest (RF), Support Vector Machines (SVM), Gaussian Process Regression (GPR), and Gradient Boosted Trees (GBTA), were leveraged to develop predictive models for wear loss and COF. The models were trained and validated using experimental data, demonstrating the efficacy of machine learning in accurately predicting tribological performance while minimizing extensive experimental trials. Among the models, GPR exhibited the highest predictive accuracy, surpassing other algorithms in forecasting wear behaviour.
Software engineering and IoT-driven systems for monitoring sustainable food practices in hospitality Anamika Ahirwar Smart Operations and Enhancing Guest Experience in the Hospitality Industry, 2025 Rapid urbanization has created problems and sustainability issues for cities, but smart cities (SC) have emerged as a hope. SC services can promote urban sustainability and life quality (QoL), increase public value, streamline everyday routines. In digital era when smart homes are becoming common, this study investigates the practical applications and implications of integrating IoT devices into kitchen environments for hospitality industry to reduce waste, optimize resources, and meet environmental goals. It focuses on an increase in demand for sustainable methods. This study inspects the application of SE and IoT technologies to track and improve sustainable food practices in hospitality processes. This chapter elaborates on how IoT-driven automation might improve convenience, efficiency, and sustainability in food preparation, storage, and consumption. This analyzes real-world use cases and developing trends to explore the impact of IoT-based Kitchen Automation Systems.
Future Trends and Emerging Sustainable Technologies in Biomedical Field Aashi Singh Bhadouria, Anamika Ahirwar Advanced Technologies for Sustainable Biomedical Applications, 2025 Biomedical engineers integrate scientific knowledge from the biological, medical, and engineering domains to solve complex healthcare problems. This results in innovations in diagnostics, personalized therapies, and healthcare technology. An increasing emphasis on efficiency and sustainability is shown by recent advancements in biodegradable materials, telemedicine, and artificial intelligence (AI). While AI enhances diagnosis accuracy and enhances pharmaceutical development, biodegradable materials reduce medical waste and its environmental impact. Telemedicine expands people's access to healthcare, which is especially important during public health crises. More and more courses are including discussions of ethics in their curricula to better equip students to deal with real-world problems in healthcare, such as patient data privacy and healthcare equity. Improving healthcare delivery and patient outcomes, the development of intelligent biomaterials and robotics showcases the game-changing possibilities of biomedical engineering. To ensure that technological advances have minimal negative impacts on the environment and maximize positive social impacts, ethical frameworks and sustainable practices are being prioritized as the field advances.
Challenges and Future Direction of Brain Imaging Studies with Focus on Understanding Shital R. Shegokar, Anamika Ahirwar Brain Informatics Technology, 2025 Neuroimaging takes developed into the most critical innovations in expanding the knowledge of understanding function, especially in nervous disorders. Procedures such as fMRI and EEG are becoming more and more essential for studying intelligence movement and its connectivity in equally healthy individuals and medical cases. Despite these, there are a number of obstacles that still need to be surmounted to unleash the full potential of neuroimaging. An important trade-off in current imaging techniques is between spatial and temporal resolution. Though fMRI provides high spatial resolution, it does not have adequate temporal resolution for fast neural processes. On the other hand, EEG is particularly strong on temporal resolution but has a “hopeless” localization problem. The balance this strikes on interpreting the data concerning cognitive functions is, consequently, rather hard to reach. An additional problem is the fMRI signal variability and the fact that it has been found to differ across different individuals and even within the same individuals over different sessions. Naturally, this variation directly influences the replicability and validity of findings and therefore speaks to an argument about standardization in both data acquisition and its analysis. Also, to note, the integration of different neuroimaging modalities remains a hard problem since each method delivers very specific information, which is hard to harmonize with others. Brain imaging research in the future should be based on the development of multi-modal approaches that capitalize on the strengths of fMRI, EEG, and fNIRS. Such methods will make it possible to achieve a more detailed comprehension of brain anatomy and physiology, thereby ensuring accurate data interpretation. Additionally, many advancements in machine learning, deep learning, and artificial intelligence would have profound implications for neuroimaging analysis. In this context, these technologies can assist in identifying new patterns within complex datasets, facilitate better signal processing, and help in revealing features from brain data, allowing us to develop more accurate models of the function and dysfunction of the brain.
Data Acquisition Technologies in Brain Informatics Nilesh Kharche, Anamika Ahirwar Brain Informatics Technology, 2025 This chapter introduces the importance of data acquisition in intelligence informatics research a multi-disciplinary area involving information processing system, neurology, psychological feature skills, and informatics used to realize the operation and functioning of the brain. It examines the various data acquisition approaches which include neuro-imaging modalities, such as fMRI, EEG, and MEG non-invasive approaches, for example, multiphoton microscopy and NIRS and cutting-edge invasive approaches, for example, thick brain stimulation and micro-electrode arrays. This chapter also highlights the importance of integrating different data sets including genetic, behavioral, and neuro-physiological data and stresses the need for robust data handling as well as machine learning algorithms in dealing with big volumes of brain data.
Mastering Data Science Unravelling Patterns, Predictive Analytics for Building Intelligent Systems AA Aashi Singh Bhadouria 2026
Examining the Development of Cloud Kitchens: Consequences for the Hospitality and Tourism Industries' Future A Ahirwar Robotics in Hotel Services: Housekeeping, Reception, and Concierge Services … , 2026 2026
Software Engineering and IoT-Driven Systems for Monitoring Sustainable Food Practices in Hospitality A Ahirwar Smart Operations and Enhancing Guest Experience in the Hospitality Industry … , 2026 2026
Data Acquisition Technologies in Brain Informatics: Tools and Techniques N Kharche, A Ahirwar Brain Informatics Technology, 89-114 , 2025 2025
Challenges and Future Direction of Brain Imaging Studies with Focus on Understanding SR Shegokar, A Ahirwar Brain Informatics Technology, 471-492 , 2025 2025
Brain Informatics Technology A Ahirwar, R Bhatt, D Dhanya, R Choudhary John Wiley & Sons , 2025 2025
Future Trends and Emerging Sustainable Technologies in Biomedical Field AS Bhadouria, A Ahirwar Advanced Technologies for Sustainable Biomedical Applications, 445-482 , 2025 2025 Citations: 1
Machine Learning-Based Optimization Techniques for Enhancing Software Quality JMKKM Anamika Ahirwar, Aashi Singh Bhadouria Horizons in Computer Science Research 26, 211-.229 , 2025 2025
Triboinformatic analysis and prediction of B 4 C and granite powder filled Al 6082 composites using machine learning regression models A Aherwar, A Ahirwar, VK Pathak Scientific Reports 15 (1), 27160 , 2025 2025 Citations: 11
Predicting Novel Coronavirus Trends Using Machine Learning A Ahirwar, MS Panwar Driving Global Health and Sustainable Development Goals With Smart … , 2025 2025
Analyzing Financial Sentiments Using BERT Model: A Deep Dive Into Market Perception and Investor Behavior for Informed Investment Decisions AS Bhadouria, A Ahirwar, MS Panwar Utilizing AI and Machine Learning in Financial Analysis, 213-244 , 2025 2025
Predictive Model Approach for Enhancing Diet Management for Diabetes Patients Through Artificial Intelligence ASB Anamika Ahirwar Intelligent Systems and IoT Applications in Clinical Health, 335-366 , 2024 2024 Citations: 4
Cybersecurity Reinforcement: Harnessing AI's Power DDMSP Anamika Ahirwar Horizons in Computer Science Research 25, 67-93 , 2024 2024
THE ROLE OF DIGITAL TECHNOLOGY IN THE LEARNING PROCESS: A COMPREHENSIVE REVIEW A A. International Journal of Creative Research Thoughts (IJCRT) 12 (3), 391-393 , 2024 2024
Optimizing Mobile Ad Hoc Networks-Genetic Algorithms for Improved Data Aggregation Privacy and Intrusion Detection A Ahirwar 11th International Conference in the Series Youth 2025 India Rising, 28-33 , 2024 2024
Introduction of AI in innovative engineering A Ahirwar Innovative Engineering with AI applications, 1-22 , 2023 2023 Citations: 1
Innovative Engineering with AI applications B Ahirwar, A. , Shukla, P.K. , Shrivastava, M. , Maheshwary, P. , Gour 2023
Innovative Engineering with AI Applications A Ahirwar, PK Shukla, M Shrivastava, P Maheshwary, B Gour John Wiley & Sons , 2023 2023 Citations: 1
Intrusion Detection Technique for Effective Data Aggregation, Privacy and Genetic algorithm for MANET A Ahirwar Recent Advancements in Engineering & Technology (ICRAET-2022) , 2022 2022
MOST CITED SCHOLAR PUBLICATIONS
Multiobjective genetic algorithm and convolutional neural network based COVID-19 identification in chest X-ray images PK Shukla, JK Sandhu, A Ahirwar, D Ghai, P Maheshwary, PK Shukla Mathematical Problems in Engineering 2021, 1-9 , 2021 2021 Citations: 271
Study of techniques used for medical image segmentation and computation of statistical test for region classification of brain MRI A Ahirwar International Journal of Information Technology and Computer Science 5 (5 … , 2013 2013 Citations: 102
Enhanced SMOTE & Fast Random Forest Techniques for Credit Card Fraud Detection A Ahirwar Solid State Technology 63 (No. 6 (2020)), 4721-4733 , 2020 2020 Citations: 14
Characterization of tumor region using SOM and Neuro Fuzzy techniques in Digital Mammography A Ahirwar, RS Jadon International Journal of Computer Science and Information Technology 3 (1 … , 2011 2011 Citations: 13
Triboinformatic analysis and prediction of B 4 C and granite powder filled Al 6082 composites using machine learning regression models A Aherwar, A Ahirwar, VK Pathak Scientific Reports 15 (1), 27160 , 2025 2025 Citations: 11
Classification of Protein Structure (RMSD<= 6A) using physicochemical properties S Mishra, Y Pathak, A Ahirwar International Journal of Bio-Science and Bio-Technology 7 (6), 141-150 , 2015 2015 Citations: 8
Comparative study of machine learning models in protein structure prediction S Mishra, A Ahirwar Int J Comput Sci Inf Technol 6 (6), 5398-5404 , 2015 2015 Citations: 7
Segmentation and characterization of Brain MR image regions using SOM and neuro fuzzy techniques A Ahirwar, RS Jadon Proceedings of the First International Conference on Emerging Trends in Soft … , 2011 2011 Citations: 5
Predictive Model Approach for Enhancing Diet Management for Diabetes Patients Through Artificial Intelligence ASB Anamika Ahirwar Intelligent Systems and IoT Applications in Clinical Health, 335-366 , 2024 2024 Citations: 4
Effectiveness of Soft Computing Techniques for Medical Imaging A Ahirwar, RS Jadon 2013 5th International Conference and Computational Intelligence and … , 2013 2013 Citations: 4
FCSOFM Technique for Medical Image Segmentation A Ahirwar, RS Jadon International Journal of Bio-Science and Bio-Technology 7 (4), 277-282 , 2015 2015 Citations: 2
Measure the Effectiveness of an Innovative Scheme for Medical Imaging A Ahirwar International Journal of Computer Applications 975, 8887 , 2012 2012 Citations: 2
Future Trends and Emerging Sustainable Technologies in Biomedical Field AS Bhadouria, A Ahirwar Advanced Technologies for Sustainable Biomedical Applications, 445-482 , 2025 2025 Citations: 1
Introduction of AI in innovative engineering A Ahirwar Innovative Engineering with AI applications, 1-22 , 2023 2023 Citations: 1
Innovative Engineering with AI Applications A Ahirwar, PK Shukla, M Shrivastava, P Maheshwary, B Gour John Wiley & Sons , 2023 2023 Citations: 1
An Analytical Study on Cloud Computing DA Ahirwar, N Prajapat, S Raj BSSS Journal of Computer, 30-35 , 2021 2021 Citations: 1
Depiction the Artificial Intelligence with Machine Learning A Ahirwar Solid State Technology 63 (6 (2020)), 4734-4739 , 2020 2020 Citations: 1
A fabric way to enhance histogram equalization & support vector machine method KR Qureshi, A Ahirwar Proceedings of International Conference on Sustainable Computing in Science … , 2019 2019 Citations: 1
An analysis on feature selection method using real coded genetic algorithm (RCGA) S Mishra, A Ahirwar J. Softw. Eng. Tools & Technol. Trends 5 (1), 23-30 , 2018 2018 Citations: 1
