Analysis of Fraud Prediction and Detection Through Machine Learning
Genetic Algorithm (GA); Insurance fraud; K-means; Machine Learning
In today's world the rate of fraudulent activities has significantly elevated, because of which a need for a competent system is required. Among all the fraudulent activities insurance fraud has the most dominating rate of growth. Fraud studies have suggested, that upon identifying the similar characteristics of a fraudulent claim with the claimants, a system of forensic and data-mining technologies for fraud detection can be set up. In this, seek to define fraud and fraudster, and look at the types of fraud and followed by the consequences of fraud to financial systems. As fraud is getting widespread these days epically in the health care insurance system, dealing with this problem has become a necessity. Unsupervised machine learning algorithms such as K-Means clustering along with supervised algorithms used in machine learning, like support vector machines, logistic regression, design trees etc. can play a very vital role in binary class classifications, which would ultimately help in identifying and outreaching the desired goal of fraudulent detection. In the end, this paper specifies the best or the most appropriate model that could be used in the given dataset to produce the most accurate results, based on certain parameters of confusion metrics like accuracy, precision, and specificity. 2023 IEEE.
Upreti K.; Sharma A.; Khatri V.; Hundekari S.; Gautam V.; Kapoor A.
2023 International Conference on Network, Multimedia and Information Technology, NMITCON 2023
Institute of Electrical and Electronics Engineers Inc.
Upreti K., CHRIST(Deemed to Be University), Dept. of Computer Science, Ghaziabad, India; Sharma A., Dr. Akhilesh das Gupta Institute of Technology and Management, Department of Cse, Delhi, India; Khatri V., Bpit, Department of Cse, Delhi, India; Hundekari S., Mit Adt University, Pune, India; Gautam V., University of Warwick, United Kingdom; Kapoor A., Department of Cse, Maharaja Surajmal Institute of Technology, Delhi, India
Discovering patterns of live birth occurrence before in vitro fertilisation treatment using association rule mining
AI; ARM; artificial intelligence; association rule mining; donor; in-vitro fertilisation; IVF; market basket analysis
According to estimates, in-vitro fertilisation (IVF) is credited for the delivery of over 9 million children globally, constituting it to be a highly remarkable as well as commercialised advanced healthcare treatment. Nonetheless, the majority of IVF treatments are now constrained by factors such as expense, access and most notably, labour-intensive, technically demanding processes carried out by qualified professionals. Advancement is thus crucial to maintaining the IVF markets rapid growth while also streamlining current procedures. This might also improve access, cost, and effectiveness while also managing therapeutic time efficiently and at a reasonable cost. IVF has become a renowned technique for addressing problems like endometriosis, poor embryo development, hereditary diseases of the parents, issues with the biological function, problems with counteracting agents that harm either eggs or sperm, the limited capacity of semen to penetrate cervical bodily fluid, and lower sperm count that lead to infertility in humans. Copyright 2023 Inderscience Enterprises Ltd.
Upreti K.; Singh D.; Singh A.; Vats P.; Bhardwaj R.; Kapoor S.
International Journal of Electronic Healthcare, Vol-13, No. 4, pp. 275-294.
https://doi.org/10.1504/IJEH.2023.138250 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192711168&doi=10.1504%2fIJEH.2023.138250&partnerID=40&md5=529d302961032d90e0ec0cd62e9ca5f6
Upreti K., CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Singh D., UIT, Barkatullah University, Madhya Pradesh, Bhopal, India; Singh A., LNCT Group of Colleges, LNCT Campus Kalchuri Nagar, Raisen Road, Bhopal, India; Vats P., SCSE, Faculty of Engineering, Manipal University Jaipur, Rajasthan, Jaipur, India; Bhardwaj R., Chitkara Business School, Chitkara University, Punjab, Rajpura, India; Kapoor S., Dr. Akhilesh Das Gupta Institute of Professional Studies, Shastri Park, New Delhi, India
Progressive loss-aware fine-tuning stepwise learning with GAN augmentation for rice plant disease detection
Augmentation; Deep Learning; Plant Disease; Rice Disease; Transfer Learning
Modern technology like Artificial Intelligence (AI) must be used in the agricultural sectorif sustainable agricultural output is to be achieved. One of the most convenient strategies for resolving current and future issues is data-driven agriculture. For this, disease prediction is a major task for precise farming. For predictive analysis and precise agriculture monitoring systems, with the application of AI, Machine Learning (ML) and Deep Learning (DL) play vital roles in building a more robust system. In this work, we will design a DL-integrated rice disease prediction system to be implemented for precise farming. Improvisation of the developed model to detect rice plant diseases & pest attacks with a high level of precision. In this work, the Progressive Loss-Aware Fine-Tuning Stepwise Learning (PLAFTSL) model is proposed for disease detection. For step-wise learning fine-tuned ResNet50 model is used with the introduction of freezing and unfreezing layers. This reduces the training parameters and thus computational complexity. The introduction of the step-wise and progressive loss-aware layer will result in fast convergence and improved training efficiency during information exchange among layers respectively. Our proposed work uses a dataset from two sources. The result analysis is presented with an ablation study. Additionally, the baseline model, ResNet50, is used to display the outcomes of the ablation. The results demonstrate that the fine-tuned model results in better performance as compared to the transfer learning model. The Conditional Generative Adversarial Network (cGAN) augmentation is also added to the designed model which will improve detection effectiveness and can also manage the imbalance in input data. The model has achieved approx. 98% accuracy and outperforms better with comparative state-of-art models. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Upreti K.; Singh P.; Jain D.; Pandey A.K.; Gupta A.; Singh H.R.; Srivastava S.K.; Prasad J.S.
Multimedia Tools and Applications, Vol-83, No. 37, pp. 84565-84588.
Upreti K., CHRIST(Deemed to be University), Delhi NCR Campus, Delhi, India; Singh P., JSS Academy of Technical Education, Noida, India; Jain D., ABES Engineering College, Ghaziabad, India; Pandey A.K., ABES Engineering College, Ghaziabad, India; Gupta A., Department of CSE (AI & amp; ML), PIET Engineering College Samalkha, Haryana, India; Singh H.R., Greater Noida Institute of Technology, Greater Noida, India; Srivastava S.K., Department of Applied Computational Science & amp; Engineering, GL BAJAJ Institute of Technology & amp; Management, Greater Noida, India; Prasad J.S., Greater Noida Institute of Technology, Greater Noida, India
An IoHT System Utilizing Smart Contracts for Machine Learning -Based Authentication
Artificial intelligence; blockchain technologies; E Healthcare; Internet of Healthcare Things (IoHT); Ma-chine learning; Random Forest; Support vector machines component
The Internet of Healthcare Things (IoHT) and blockchain technologies have made it feasible to share data in a secure and effective manner, but it is still challenging to ensure the data's veracity and privacy. This paper presents a blockchain authentication method that utilizes Machine Learning (ML) techniques that use smart contracts to ensure the security and privacy of IoHT data. The process utilizes smart contracts to manage access control and ensure data integrity, and deep learning algorithms to identify and validate the accuracy of user data. Furthermore, the approach improves the resilience and dependability of the authentication process and permits secure data ex-change between multiple IoHT systems. The proposed approach provides a potentially revolutionary solution to enhance the safety and confidentiality of IoHT data. It has the potential to fundamentally change how healthcare is provided in the future. 2023 IEEE.
Upreti K.; Vats P.; Borkhade G.; Raut R.D.; Hundekari S.; Parashar J.
2023 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2023 - Proceedings, pp. 196-201.
Upreti K., CHRIST(Deemed to Be University), Dept. of Computer Science, Delhi NCR, Ghaziabad, India; Vats P., Scse Manipal University Jaipur, Dept. of Cse, Rajasthan, Jaipur, India; Borkhade G., Bharati Vidyapeeth College of Engineering, Dept. of Electronics & Telecommunication Engg, Maharashtra, Navi Mumbai, India; Raut R.D., Sant Gadge Baba Amravati University, Pgtd Applied Electronics, Maharashtra, Amravati, India; Hundekari S., Mit Adt University Loni Kalbhor, Pune, Pune, India; Parashar J., Adgitm, Dept. of Computer Science & Engg., New Delhi, India
Synergizing Senses: Advancing Multimodal Emotion Recognition in Human-Computer Interaction with MFF-CNN
Deep Learning and Emotion Classification; Facial Expressions; Human-Computer Interaction; MFF-CNN (Multimodal Fusion and Convolutional Neural Network); Multimodal Emotion Recognition; Speech Signals
Optimizing the authenticity and efficacy of interactions between humans and computers is largely dependent on emotion detection. The MFF-CNN framework is used in this work to present a unique method for multidimensional emotion identification. The MFF-CNN model is a combination of approaches that combines convolutional neural networks and multimodal fusion. It is intended to efficiently collect and integrate data from several modalities, including spoken words and human facial expressions. The first step in the suggested system's implementation is gathering a multimodal dataset with emotional labels added to it. The MFF-CNN receives input features in the form of retrieved facial landmarks and voice signal spectroscopy reconstructions. Convolutional layers are used by the model to understand hierarchies spatial and temporal structures, which improves its capacity to recognize complex emotional signals. Our experimental assessment shows that the MFF-CNN outperforms conventional unimodal emotion recognition algorithms. Improved preciseness, reliability, and adaptability across a range of emotional states are the outcomes of fusing the linguistic and face senses. Additionally, visualization methods improve the interpretability of the model and offer insights into the learnt representations. By providing a practical and understandable method for multimodal emotion identification, this study advances the field of human-computer interaction. The MFF-CNN architecture opens the door to more organic and psychologically understanding human-computer interactions by showcasing its possibilities for practical applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Upreti K.; Vats P.; Malik K.; Verma R.; Divakaran P.; Gangwar D.
Lecture Notes in Networks and Systems, Vol-1047 LNNS, pp. 279-288.
Springer Science and Business Media Deutschland GmbH
Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR, Ghaziabad, India; Vats P., Department of Computer Science and Engineering, SCSE, Manipal University Jaipur, Rajasthan, Jaipur, India; Malik K., School of Law, CHRIST (Deemed to Be University), Delhi NCR, Ghaziabad, India; Verma R., School of Business and Management, CHRIST (Deemed to Be University), Delhi NCR, Ghaziabad, India; Divakaran P., School of Business and Management, Himalayan University, Arunachal Pradesh, Itanagar, India; Gangwar D., Department of Management, Babu Banarasi Das Institute of Technology and Management, Lucknow, India
A Novel Framework for Harnessing AI for Evidence-Based Policymaking in E-Governance Using Smart Contracts
Artificial Intelligence (AI); Citizen engagement; Data analysis; E-governance; Evidence-based policymaking; Personalized services; Prediction
Harnessing AI for evidence-based policymaking in e-governance has the potential to revolutionize the way governments formulate and implement policies. By leveraging AI technologies, governments can analyze vast amounts of data, extract valuable insights, and make informed decisions based on evidence. This chapter explores the various ways in which AI can be employed in e-governance to facilitate evidence-based policymaking. It discusses the use of AI algorithms for data analysis and prediction, enabling governments to identify patterns, trends, and emerging issues from diverse data sources. Moreover, AI-powered tools can enhance citizen engagement and participation, by facilitating data-driven decision-making processes and providing personalized services. Additionally, AI can assist in policy evaluation and impact assessment, by automating the collection and analysis of data, thus enabling governments to measure the effectiveness of their policies in real-time. Furthermore, AI can contribute to enhancing transparency and accountability in e-governance, by automating processes such as fraud detection and risk assessment. Despite the immense potential, the adoption of AI in e-governance must address challenges such as data privacy, algorithmic bias, and ethical considerations. This chapter concludes by emphasizing the importance of building trust, ensuring fairness, and promoting responsible AI practices to maximize the benefits of AI in evidence-based policymaking for e-governance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Upreti K.; Verma A.; Mittal S.; Vats P.; Haque M.; Ali S.
Communications in Computer and Information Science, Vol-1921 CCIS, pp. 231-240.
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Verma A., Dr. Akhilesh Das Gupta Institute of Technology and Management, Delhi, India; Mittal S., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, Rajpura, India; Vats P., SCSE, Manipal University Jaipur, Rajasthan, Jaipur, India; Haque M., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Dr. D.Y. Patil Vidyapeeth, Pune (Deemed to be University), Pune, India; Ali S., Faculty of Digital Business, Lithan Academy (eduCLaaS Pte Ltd.), Singapore, Singapore
A Comparative Analysis of LSB & DCT Based Steganographic Techniques: Confidentiality, Contemporary State, and Future Challenges
Data Hiding; Data Security; Image Steganography; Imperceptibility; Information Hiding; LSB insertion; Steganalysis
In order to maintain anonymity and security, the steganography is the technique of cloaking confidential data within what seems like harmless digital material. Several steganographic methods have been established devised over time, but those centered around the discrete cosine transformation (DCT) and the least significant bit (LSB) have drawn the most consideration. In this study, two common steganographic methods are compared and contrasted with an emphasis on the secrecy they can keep, the usage they are now receiving, and any potential difficulties in the future. As an alternative, the DCT-based method uses the frequency domain properties of cover media to obfuscate hidden information. Since it spreads the concealed information across several frequency coefficients, it provides greater security than LSB-based techniques. The resilience and imperceptibility of the concealed data are improved by a variety of DCT-based algorithms, such as the modified quantization and matrix encoding approaches, which we explore in detail. We also give a general summary of both approaches'current state in terms of their application, constraints, and areas in which they may be used. We evaluate the benefits and drawbacks of each approach, considering elements like payload size, computing difficulty, and detection resistance. 2023 IEEE.
Upreti K.; Verma A.; Parashar J.; Vats P.; Verma A.; Singh J.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 1581-1588.
Upreti K., CHRIST (Deemed to Be University) Delhi NCR, Department of Computer Science, Ghaziabad, India; Verma A., Department of Computer Science & Engineering ADGITM, New Delhi, India; Parashar J., Department of Computer Science & Engineering ADGITM, New Delhi, India; Vats P., SCSE Manipal University Jaipur Jaipur, Department of CSE, Rajasthan, India; Verma A., KIET Group of Institutions Delhi -NCR, Department of Computer Applications, Ghaziabad, India; Singh J., School of CSE and Technology, Bennett University Greater, Noida, India
Pandemic Pulse: Unveiling Insights with the Global Health Tracker Through AI and ML
Business intelligence; COVID-19; Dashboard; Data exploration; Data visualization; Data visualization techniques; Tableau
The current study highlights the importance of data analysis by applying data visualization tools to help you understand the pandemic disease informational component, and how it can be converted into knowledge that might enhance decision-making processes. In Tableau, a software for displaying data, researchers have incorporated a pandemic disease informational component from Coursera to improve assessment and selection. After becoming familiar with the data and the data visualization technological advances, some of it will be expected to conduct an initial investigation to identify significant changes in the data that is under consideration, compile and present this pandemic disease informational component, and enhance the corporate decision-making process. This issue for inquiry highlights the significance of knowledge examination via the use of communication visualization applications to aid in your comprehension of the pandemic disease informational component as well as how it may be changed into knowledge that may enhance the process of arriving at decisions. The creators of the knowledge representation computation application scenario used data from Coursera to improve their studies and make decisions. One will need to conduct an exploratory inquiry to find notable trends within the data after familiarizing oneself with it by utilizing visualization programs to compile and distribute this data to improve the company's decision-making procedures. This specific software is designed to be utilized in an early administrative duties course, an undergraduate accounting data structure course, or a data analytics-only educational program as a basic introduction to an informative visualization computer application. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Upreti K.; Verma V.S.; Verma A.; Vats P.; Patil S.; Kuwar V.
Lecture Notes in Networks and Systems, Vol-991 LNNS, pp. 191-203.
Upreti K., CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Verma V.S., Bhagwan Parshuram Institute of Technology, Delhi, Rohini, India; Verma A., Dr. Akhilesh Das Gupta Institute of Professional Studies, Delhi, India; Vats P., Computer Science and Engineering, Manipal University Jaipur, Rajasthan, Jaipur, India; Patil S., Dr. D. Y. Patil Vidyapeeth, Centre for Online Learning, Pimpri, Pune, India; Kuwar V., Dr. D. Y. Patil Vidyapeeth, Centre for Online Learning, Pimpri, Pune, India
A Comprehensive Study for Application of Blockchain Technologies for the Decentralized Grid Utilization Possibilities
blockchain-based applications; Distributed ledger; intelligent medical care; peer-to-peer system
This thorough research explores the world of blockchain technology and its significant effects on the use of decentralized grids. Decentralized networks have shown promise in addressing the rising need for renewable energy and effective resource management. Blockchain, a technology based on distributed ledgers, presents creative approaches to improve grid oversight, enable through peer-to- energy trade, and guarantee openness and protection in the management of the grid. This paper investigates the advantages and disadvantages of using blockchain in decentralized grids. From trading in renewable energy to grid optimization and demand response, we examine a variety of use cases and applications. We offer insight into the practical viability and scalability of blockchain-based solutions through a thorough analysis of real-world deployments and case reports. We also address the legislative and technological challenges to be solved before blockchain technology can realize its full potential in decentralized grid setups. Our research strongly emphasizes that regulatory architectures, seamless integration, and standardization all contribute to supporting the harmonious adaptation of blockchain technology worldwide. This study provides lawmakers, industry players, and investigators in the efforts to build a sustainable and effective energy future with an informative tool: it will shed light on the potential and constraints presented by blockchain technology in a decentralized environment of grid usage. 2025 IEEE.
Upreti, Kamal; Divakaran, Prakash; Chaturvedi, Vandana Mishra; Upreti, Shitiz; Vats, Prashant; Goyal, Kamal
2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025;pp.305-310
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/IC363308.2025.10956744 https://www.scopus.com/pages/publications/105003905529?origin=resultslist
Upreti K., Christ (Deemed to be University), Dept. of Computer Science, Delhi NCR, India; Divakaran P., Himalayan University, Arunachal Pradesh, Itanagar, India; Chaturvedi V.M., D Y Patil Deemed to be University, Navi Mumbai, India; Upreti S., Maharishi Markandeshwar (Deemed to be University), Department of Management (MMIM), Mullana, Haryana, Ambala, India; Vats P., Manipal University Jaipur, Dept. of Cse, Rajasthan, Jaipur, India; Goyal K., Pimpri Chinchwad University, Pune, India
AI-Optimized Erection of Landslide-Resistant Retaining Structures Through Heterogeneous Composite Nanomaterials: A Computerized Algorithmic Breakthrough
AI-optimized; Composite Nanomaterials; Computerized and Algorithmic; Erection; Heterogeneous; Landslide-Resistant; Retaining Structures
The proposed work relates to the field of environmental protection and ensuring the environmental safety of urban development and the population from erosion and landslide phenomena. It can be used to create territorial plans for the development of recreational areas in areas subject to these natural and man-made impacts. The technical result of the proposed work is to ensure the reduction of natural and man-made impacts on urban and similar settlements through the use of new technological solutions for the creation of structures using heterogeneous composite nanomaterials. A technical result is achieved by equipping the territory with buildings and structures, creating a base and a soil-reinforced array, with the location of blocks in it, made as soil-filled shells, on the basis, a soil-filled shell-stay-base is mounted with a soil-filled shell base, a rigid frame is mounted and the front wall, which is fixed with the second attachment point, fix the soil-filled shell, its upper part is made waterproof and equipped with a water outlet through the second attachment point, to which the rigid frame and the front wall are fixed, they are fixed with the third fastening unit, to which a soil-filled shell-plate with a storm drain of one or more arm tapes with a drainage system filled with a sorbent and placed in a waterproof shell. The front wall is additionally covered with a polymer material with seeds. All structural elements are made of heterogeneous composite nanomaterials. As a polymeric material with seeds, the material PINEMA is used. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Upreti, Kamal; Divakaran, Prakash; Patil, S.S.; Dadhich, Priyanka; Gautam, Anjali; Vats, Prashant
Communications in Computer and Information Science;Volume;2072 CCIS;pp.50-62
https://doi.org/10.1007/978-3-031-84062-3_5 https://www.scopus.com/pages/publications/105002379033?origin=resultslist
Upreti K., Department of Computer Science, Christ University, Delhi NCR, Ghaziabad, India; Divakaran P., Department of Management, Himalayan University, Arunachal Pradesh, Itanagar, India; Patil S.S., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Dr. D.Y. Patil Vidyapeeth, Pune (Deemed to be University), Pimpri-Chinchwad, India; Dadhich P., Department of Computer Science, DPG Degree College, Sector-43, Haryana, Gurugram, India; Gautam A., Department of Computer Science, DPG Degree College, Sector-43, Haryana, Gurugram, India; Vats P., Department of CSE, SCSE, Manipal University Jaipur, Rajasthan, Jaipur, India
AI-Enabled Early Detection of Chemo-Induced Cardiotoxicity Patterns Using ECG Time Series Data
cardio-oncology; cardiotoxicity; chemotherapy; ECG; precision medicine
Objectives: Chemotherapy-induced cardiotoxicity is still a major clinical problem, usually appearing subclinically before structural or symptomatic cardiac dysfunction appears. Standard surveillance methods use imaging and biomarkers, which are time-intensive and money-intensive and can only identify damage at more advanced levels. Electrocardiography (ECG) provides a low-cost, non-invasive method that can detect early electrophysiological changes but is not fully utilized in cardio-oncology. The present work was designed to build an explainable machine learning model for predicting chemo-like cardiotoxicity patterns at an early stage from single-lead ECG signals. Methods: A public ECG data set (n=4997 segments) underwent preprocessing and was converted to 18 temporal, morphologic, and spectral features. Two ensemble learning algorithmsRandom Forest and XGBoostwere trained and validated with stratified splits. Model performance was assessed with ROCAUC, PRAUC, and F1-score with 1000 bootstrap resampling. Feature interpretability was evaluated through permutation importance and SHAP analysis. Results: Both models scored near-perfect classification (ROCAUC and PRAUC>0.99, F1-score ? 0.986). Spectral entropy, band3 (high-energy frequency), QT surrogate, and peak count were the top features ranking alongside early cardiotoxicity indicators like repolarization instability and autonomic imbalance. Conclusions: The feature-driven, interpretable ML architecture suggested here shows that single-lead ECG has the potential to be an affordable and clinically relevant tool for the early detection of chemotherapy-induced cardiotoxicity. The method provides a feasible route toward implementation in precision cardio-oncology, particularly in resource-poor or ambulatory environments. 2025
Upreti, Kamal; George, Jossy P.; Malik, Khushboo; Radhakrishnan, G. V.; Ga-B?aszczykowska, Agnieszka
American Journal of Clinical Oncology: Cancer Clinical Trials;
https://doi.org/10.1097/COC.0000000000001280 https://www.scopus.com/pages/publications/105029467813?origin=resultslist
Upreti K., Department of Computer Science, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India; George J.P., Department of Computer Science, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India; Malik K., Department of Computer Science, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India; Radhakrishnan G.V., Department of Computer Science, KIIT School of Management (KSOM), Bhubaneswar, India; Ga-B?aszczykowska A., School of Law, War Studies University, Warsaw, Poland
Optimized Hybrid Prognostics Using Hynetreg Model for Infertility Prediction
Alpha-Feto-Protein (AFP); Anti-Mlerian Hormone (AMH); Blood Urea Nitrogen (BUN); Follicle Stimulating Hormone (FSH); Idiopathic Female Infertility (IFI); Luteinizing Hormone (LH); multi-layer perceptron (MLP); poor ovarian response (POR); recurrent reproductive failure (RRF); total motile sperm count (TMSC)
This paper develops an optimized hybrid approach to predict infertility with the HyNetReg Model. The HyNetReg Model combines deep feature extraction by using neural networks with logistic regression with regularization. It uses both hormonal and demographic information of 100 participants to clarify intricate interlinkages between demographic factors and salient hormonal levels, such as Luteinizing Hormone, Follicle Stimulating Hormone, Anti-Mlerian Hormone, and Prolactin, and the ability of these same factors to affect fertility outcomes. It applies heavy data pre-processing including normalization, missing values imputation, and class imbalance handling through oversampling techniques. A multi-layer neural network is utilized to extract features for the reduction of complex, non-linear interaction among the input variables. Then, regularized logistic regression is applied for classification on the same features. Performance evaluation metrics, including accuracy, precision, recall, F1-score, and ROC curve analysis, demonstrate the superiority of the HyNetReg Model over traditional logistic regression. The ROC curve was specifically utilized to assess the models discrimination ability between infertile and fertile cases by plotting the true positive rate (sensitivity) against the false positive rate (1-specificity). A higher Area Under the Curve indicated that the model effectively distinguished infertility risks based on hormonal and demographic features. The results indicate that the model can recover very slight interdependencies of hormones and influences of demographics, making it suitable for modeling multi-factorial determinants of infertility and holding significant implications for clinical decision-making. 2025 Oriental Scientific Publishing Company. All rights reserved.
Upreti, Kamal; George, Jossy; Hundekari, Sheela; Alam, Mohammad Shabbir
Biomedical and Pharmacology Journal;Volume;18;Issue;2;pp.1272-1288
https://doi.org/10.13005/bpj/3168 https://www.scopus.com/pages/publications/105010881684?origin=resultslist
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; George J., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Hundekari S., Department of Computer Science and Engineering, Pimpri Chinchwad University, Maval Talegaon, Maharashtra, Pune, India; Alam M.S., Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan, Saudi Arabia
Automated Brain Tumor Segmentation in MRI Using AI for Improved Neurodiagnostics
Classification; Diagnosis; Early Detection; Machine Learning; MRI; Neuro-images
Early and accurate classification of brain tumors plays a pivotal role in clinical decision-making and treatment planning. Manual methods are time-intensive and prone to variability, creating a need for robust automated solutions. This study aims to classify brain tumors from MRI scans using artificial intelligence techniques, specifically Logistic Regression (LR) and Support Vector Machines (SVM) with Radial Basis Function (RBF) kernels. The dataset, sourced from The Cancer Imaging Archive (TCIA), includes four classes: Meningioma, Glioma, Hypothalamic tumor, and No tumor. Preprocessing involved dimensionality reduction using Principal Component Analysis (PCA) to retain dominant features. Models were trained on an 80:20 train-test split, with LR achieving 99.83% training and 78.91% testing accuracy, while SVM performed better with 93.85% training and 81.88% testing accuracy. Error analysis revealed 104 misclassified samples, primarily due to structural similarity among tumor types. The findings suggest that SVM offers superior classification performance, and the study recommends further enhancement through deep learning models like Convolutional Neural Networks (CNNs) for improved diagnostic accuracy. 2025 Oriental Scientific Publishing Company. All rights reserved.
Upreti, Kamal; George, Jossy; Malik, Khushboo
Biomedical and Pharmacology Journal;Volume;18;Issue;2;pp.1257-1271
https://doi.org/10.13005/bpj/3167 https://www.scopus.com/pages/publications/105011041787?origin=resultslist
Upreti K., Department of Computer Science and Engineering, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India; George J., Department of Computer Science and Engineering, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India; Malik K., Department of Law, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India
Polycystic Ovary Syndrome Diagnosis: The Promise of Artificial Intelligence for Improved Clinical Accuracy
Artificial Intelligence; Endocrine disorders; Hyperandrogenism; Infertility; Polycystic ovaries; Rotterdam Criteria
PCOS is an endocrine illness that affects 610% of women worldwide. It can cause a variety of symptoms, including irregular menstruation periods, ovarian cysts, and hyperandrogenism. Its lack of defined biomarkers, overlapping symptoms, and heterogeneity make diagnosis difficult. By studying hormone profiles, identifying patterns difficult to see with conventional approaches, and offering great precision and accuracy, AI and ML techniques are transforming diagnostic difficulties. Hybrid models in the list include SWISS-AdaBoost and ensemble learning techniques that have accuracies up to 98% enabling early diagnosis along with appropriate treatment strategies. Early detection by technologies such as AI will prevent significant health complications that are PCOS-related, such as infertility, type II diabetes, or cardiovascular diseases. This study depicts the transformative role of the application of AI in diagnosing cases of PCOS and highlights the possibility of facilitating clinical decision-making, reducing potential diagnostic delays, and enhancing improvements in patient outcomes. Future research should hence be directed towards the establishment of AI within healthcare with consideration of validation, reliability, and ethical considerations to maximize its use in clinical practice. 2025 Oriental Scientific Publishing Company. All rights reserved.
Upreti, Kamal; George, Jossy; Upreti, Shitiz; Mahajan, Shubham
Biomedical and Pharmacology Journal;Volume;18;Issue;1;pp.353-372
https://doi.org/10.13005/bpj/3093 https://www.scopus.com/pages/publications/105002833893?origin=resultslist
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; George J., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Upreti S., Department of IT & Management, Maharishi Markandeshwar (Deemed to be University), Haryana, Mullana, Ambala, India; Mahajan S., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India
Spatiotemporal analysis and intensity prediction of forest fires using cuckoo search hybrid models
Fire Intensity Classification; Forest Fire Prediction; Kernel Density Estimation; Spatiotemporal Analysis; Uttara Kannada
Forest fire forecasting is a critical aspect of environmental conservation and ecological risk management, particularly in biodiversitysensitive areas like Uttara Kannada, India. In this research, this article suggests a new hybrid modeling ap-proach that combines Cuckoo Search Optimization (CSO) with ensemble machine learning techniques, namely Random Forest (RF) and XGBoost (XGB), for forecasting fire intensity levels. Known as CSORF and CS-XGB, the hybrid models were trained and validated against a spatiotemporally dense dataset from 2009 to 2024, with primary environmental, topographic, and anthropogenic predictors. Aside from classification modeling, spatiotemporal analyses such as Kernel Density Estimation (KDE), seasonal fire patterns, and influence studies on features were performed to determine high-risk seasons and areas. CSO was used to automate the hyperparameter tuning process for both classifiers, yielding a significant boost in performance. The CS-XGB model registered the top accuracy of 99.49%, better than CSORF's 98.99%. Feature importance testing confirmed ecological significance, and humidity, temperature, and rainfall were the top-ranked variables. The work adds a scalable and precise prediction model that can assist in early warning systems and forest manage-ment practices. Kamal Upreti et al.
Upreti, Kamal; Hundekari, Sheela; Date, Saroj; Tiwari, Akhilesh; Radhakrishnan, Ganesh V.; Shankar, Uma
International Journal of Basic and Applied Sciences;Volume;14;Issue;1;pp.181-190
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.14419/9pk3h895 https://www.scopus.com/pages/publications/105005538496?origin=resultslist
Upreti K., Department of Computer Science, Christ University, Delhi NCR, Ghaziabad, India; Hundekari S., School of Computer Applications, Pimpri Chinchwad University, Pune, India; Date S., Department of Artificial Intelligence and Data Science, CSMSS Chh. Shahu College of Engineering, Chh. Sambhajinagar, Maharashtra, India; Tiwari A., Department of Business and Management, Christ University, Delhi NCR, Ghaziabad, India; Radhakrishnan G.V., Department of Economics and Finance, KIIT Univeristy, Bhubaneswar, India; Shankar U., Faculty of Management and Social Sciences, Qaiwan International University, Kurdistan, Sulaimanyah, Iraq
A Study on Gynecological Cancers Using Artificial Intelligence: A Revolutionary Approach
analysis; artificial intelligence; detection; Gynecological cancer; prediction
An examination of the role of artificial intelligence (AI) in ovarian, cervical, and breast cancer early detection and management is presented in this paper. Artificial intelligence (AI) can improve diagnostic accuracy, streamline treatment protocols, and facilitate personalized medicine approaches by leveraging advancements in machine learning (ML) and deep learning (DL). Various Artificial Intelligence models have demonstrated success in improving the outcomes of cancer diagnostics, including their ability to distinguish benign from malignant tumors. Technology challenges and ethical issues related to the integration of AI into clinical practice are also discussed in the review. Specifically, we want to illustrate how artificial intelligence can lead to better prognoses and reduced mortality rates for cancer patients by enhancing early detection capabilities. 2025 IEEE.
Upreti, Kamal; Hunekari, Sheela; Upreti, Shitiz; Rai, Ashish Kumar; Alam, Mohammad Shabbir; Nasir, Mohammad Shahnawaz
2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025;pp.259-263
https://doi.org/10.1109/IC363308.2025.10957236 https://www.scopus.com/pages/publications/105003909686?origin=resultslist
Upreti K., Christ University, Dept. of Computer Science, Delhi- NCR Campus, Ghaziabad, India; Hunekari S., School of Engineering and Technology, Pimpri Chinchwad University, Maharashtra, India; Upreti S., Maharishi Markandeshwar (Deemed to be University), Department of Management, Mullana, Haryana, Ambala, India; Rai A.K., Ramchandra Chandravansi University, Faculty of Commerce & Management, Jharkhand, India; Alam M.S., Cmj University, Dept. of Computer Science, India; Nasir M.S., Cmj University, Dept. of Computer Science, India
The Evolution of Cloud Computing: A Study of Aspirational Technologies and Practical Achievements
Aspirations vs. Achievements; Cloud Adoption Challenges; Cloud Computing; Compliance; Data Governance; Digital Transformation; Regulations; Scalability; Security and Privacy
Cloud computing has transformed the digital landscape, providing scalability, cost efficiency, and seamless access to computing resources. Yet, there is a gap between its theoretical aspirations and real-world achievements because of data privacy concerns, regulatory compliance, vendor lock-in, and performance bottlenecks. This paper critically examines these disparities through case studies, technological breakthroughs, and industry trends as a guide to understanding these impediments to cloud adoption. The study reviews the theoretical background of cloud computing, which include models related to deployment and service, and illustrates its successes in performance, reliability, and security. Persistent barriers, however, mean security and compliance are insecure while cost unpredictability remains a concern for organizations in using the maximum potential of a cloud. The study proposes strategic solutions that can serve to bridge the gap in terms of hybrid and multi-cloud adoption, AI-driven security frameworks, regulatory compliance automation, and various cost optimization techniques. Emerging trends like quantum computing, edge computing, and green cloud initiatives are shaping the future of cloud computing. By implementing these solutions, more is made out of the potential of cloud computing in securing a more efficient and sustainable digital infrastructure. 2025 IEEE.
Upreti, Kamal; Jain, Rituraj; Kumar, Raj; Goyal, Kamal; Deepika; Gupta, Komal
2025 IEEE 14th International Conference on Communication Systems and Network Technologies, CSNT 2025;pp.898-903
https://doi.org/10.1109/CSNT64827.2025.10968661 https://www.scopus.com/pages/publications/105004549931?origin=resultslist
Upreti K., Dept. of Computer Sciecne, CHRIST University, DelhiNCR, India; Jain R., Dept. of IT, Marwadi University, Gujarat, Rajkot, India; Kumar R., Gautam Buddha University, U.P, Gautam Budh Nagar, India; Goyal K., Pimpri Chinchwad University, Pune, India; Deepika, Dept. of Computer Science & Engg., Maharishi MM, Haryana, Ambala, India; Gupta K., Accenture, Bangalore, India
Upreti, Kamal; Kumar, Nishant; Alam, Mohammad Shabbir; Nasir, Mohammad Shahnawaz; Samanta, Debabrata
Big Data Analytics and Intelligent Applications for Smart and Secure Healthcare Services;pp.vii-xv
https://www.scopus.com/pages/publications/85218293720?origin=resultslist
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR Campus, Uttar Pradesh, Ghaziabad, India; Kumar N., School of Business and Management, Christ University, Bangalore, India; Alam M.S., Jazan University, Saudi Arabia; Nasir M.S., Jazan University, Saudi Arabia; Samanta D., Department of Computing and Information Technologies, Rochester Institute of Technology, European Union
Big Data Analytics and Intelligent Applications for Smart and Secure Healthcare Services
The book provides a comprehensive discussion for utilizing computational models such as artificial neural networks, agent-based models, and decision field theory, for reliability engineering. It further presents optimization solutions for smart and secure healthcare services. The text showcases how to predict the failure and repair rates of healthcare subsystems using computational intelligence. This book: Explores how data-driven methodologies and advanced computational intelligence are revolutionizing the healthcare industry, promoting efficiency, accessibility, and sustainability Highlights the pivotal role that big data analytics plays in harnessing vast amounts of patient records, clinical information, and real-time medical data to provide timely insights for healthcare professionals and policymakers Discusses the integration of artificial intelligence and machine learning techniques in healthcare, with a focus on revolutionizing disease detection, treatment planning, and resource allocation Lays the foundation for developing sustainable healthcare systems that are adaptable to long-term challenges, such as population growth, emerging diseases, and resource constraints Covers computational intelligence techniques, like fuzzy logic, neural networks, and evolutionary computations, emphasizing their role in solving complex, data-driven healthcare problems Includes topics like data management, visualization, protection, and complex adaptive systems, as well as hybrid computational intelligence techniques for synergistic problem-solving strategies This volume will serve as an ideal text for senior undergraduates, graduate students, and academic researchers in fields including electrical engineering, electronics and communications engineering, computer engineering, and mathematics. 2025 selection and editorial matter, Kamal Upreti, Nishant Kumar, Mohammad Shabbir Alam, Mohammad Shahnawaz Nasir and Debabrata Samanta; individual chapters, the contributors.
Big Data Analytics and Intelligent Applications for Smart and Secure Healthcare Services;pp.1-244
https://doi.org/10.1201/9781003508618 https://www.scopus.com/pages/publications/85218301140?origin=resultslist
Humancomputer interaction for cognitive, emotional and learning well-being
Adaptive learning; AI-Driven systems; Cognitive well-being; Emotion-aware technology; Emotional well-being; Humancomputer interaction (HCI)
HumanComputer Interaction (HCI) has revolutionized the way humans engage with technology, shaping cognitive, emotional, and learning experiences. This chapter explores HCI's impact on well-being, focusing on cognitive load reduction, emotional stability, and adaptive learning. HCI technologies such as AI-driven decision support, emotion-aware systems, and personalized education platforms enhance user engagement by fostering efficiency and well-being. Cognitive well-being benefits from AI-powered cognitive tools that improve memory, decision-making, and mental agility. Emotional well-being is facilitated by affect-sensitive systems, digital therapeutics, and social HCI that reduce stress and increase emotional engagement. Adaptive learning systems, gamification, and assistive technologies also ensure inclusive education by making learning more personalized and accessible to special needs students. Future HCI trends involve neuroadaptive interfaces, wearable-integrated health technology, and AI-based mental health solutions that all improve personalization and user experience. Yet ethical issues, such as data privacy, digital addiction, and algorithmic biases, need to be addressed in order to maintain responsible technology utilization. Achieving balance between digital and physical interactions is important in the preservation of general well-being. The future of HCI, where it becomes empathetic, adaptive, and ethical interfaces, is a future that sees technology enhance not just efficiency but cognitive, emotional, and learning well-being. By making ethical AI design and user-centric experiences a priority, HCI will continue to unleash human potential and develop durable, well-being-oriented technological solutions. 2026 Elsevier Inc. All rights reserved.
Upreti, Kamal; Kumari, Puja; Shankar, Uma; Radhakrishnan, G.; Udhaya, S.K.; Malik, Khushboo
Intelligent Systems for Neurocognition and Human-Robot-Computer Interaction;pp.43-65
https://doi.org/10.1016/B978-0-443-41660-6.00008-9 https://www.scopus.com/pages/publications/105023946368?origin=resultslist
Upreti K., Department of Computer Science, Christ University, Uttar Pradesh, Ghaziabad, India; Kumari P., Department of Psychology and Mental Health, Gautam Buddha University, Uttar Pradesh, Greater Noida, India; Shankar U., Ramcharan School of Leadership, Dr. Vishwanath Karad MIT World Peace University, Maharashtra, Pune, India; Radhakrishnan G., KIIT, Kalinga School of Management, Odisha, Bhubaneswar, India; Udhaya S.K., Department of Computer Science and Engineering, Easwari Engineering College, Tamil Nadu, Chennai, India; Malik K., School of Law, Christ University, Delhi NCR, India