Computer Engineering, Artificial Intelligence, Computer Science, Computational Theory and Mathematics
19
Scopus Publications
Scopus Publications
EcoSense: TinyML Sensor Fusion for Smart Environments Shailja Tripathi, C. L. P. Gupta, Vivek Kumar 2026 3rd International Conference on Advancements and Key Challenges in Green Energy and Computing Akgec 2026, 2026
Hands-on practices, reflection on data wisdom with AI principles: A review Rishu Kumar, C.L.P. Gupta, Anuj Singh Handbook of AI in Engineering Applications Tools Techniques and Algorithms, 2025 Addressing the evolving landscape of artificial intelligence (AI) ethics, this chapter delves into the concept of ethical construction and its evaluation. It argues that AI ethics has entered a pivotal third wave, centering on the operationalization of ethical principles. Despite the prominence of ethics in AI discourse, its application often eludes data scientists, necessitating operationalization. This chapter elucidates this paradigm shift by aligning AI ethics principles with the operational framework of AI-based digital or e-products/services, integrating a transparent governance model to delineate operational responsibilities. Through pragmatic, abstract, and political counterarguments, the authors navigate the complexities of AI ethics operationalization. The chapter concludes with a discussion of the formidable challenges surrounding the operationalization of AI ethics, aiming to provide insights for policymakers and practitioners navigating the rapidly evolving AI landscape.
An Intelligent Systems for Heart Sound Signal Analysis: A Survey Manu Saraswat, Chhote Lal Prasad Gupta Proceedings of the 11th International Conference on Bio Signals Images and Instrumentation Icbsii 2025, 2025 Heart failure is the leading factor of mortality worldwide. Cardiac auscultation is one of oldest diagnostic methods to explore the condition of the heart. Mechanical activities of heart initiated by biopotentials generated by the Sino-Atrium (SA) node of the heart causes non-stationery and non-deterministic signals commonly known as Heart Sound Signal (HSS) and the visual illustration of HSS is termed as Phonocardiogram (PCG) while recorded by an electronic stethoscope. For assessing a variety of cardiac anomalies, such as anatomical defects of the heart and valvular cardiac disease, congestive heart failure, and heart rate and rhythm, PCG can offer the most useful diagnostic data. The S1 and S2 heart sounds, which are the two loudest heart sounds, often make up PCG. In both diseased and non-pathological circumstances, the PCG signal may contain murmurs, the S3 and S4 heart sounds as well as other unusual noises including clicks, snaps, and rubs.Furthermore, in past few years, it was effectively utilized for heart sound analysis. The current study is to attempt a thorough brief to summarize articles on heart sound evaluation with deep learning released in 2017–2023, since the majority of review works on the subject were completed prior to 2017. In addition to providing insights into the developments and potential avenues for upcoming research on deep learning for the analysis of cardiac sounds, this study contrasts deep learning with conventional machine learning.
An Intelligent Systems for Heart Sound Signal Analysis Manu Saraswat, Chhote Lal Prasad Gupta Icoicc 2025 3rd International Conference on Intelligent and Cloud Computing, 2025 Globally, heart failure is the leading cause of death. One of the first diagnostic techniques for examining the state of the heart is cardiac auscultation. The heart's mechanical activity, which is triggered by biopotentials produced by the Sino-Atrium (SA) node, results in non-stationary and non-deterministic signals known as Heart Sound Signals (HSS). When captured by an electronic stethoscope, the graphical representation of HSS is known as phonocardiogram (PCG). PCG can provide very helpful diagnostic information when evaluating a range of cardiac abnormalities, like valvular heart disease, irregular heartbeat, cardiac decompensation, and anatomical defects in the heart. PCG is composed of the first and second (S1, S2) heart sounds, which are frequently the two loudest heart sounds. In both diseased and non-pathological situations, the PCG signal may comprise murmurs, the third and fourth cardiac sounds (S3), and other odd sounds like clicks, snaps, and rubs.PCG analysis is effective for non-invasive cardiovascular disease diagnosis. The thesis is on creating an Automatic Computerised Auscultation (ACA) for effective cardiac state monitoring. ACA provides diagnostic and prognostic data in a non-invasive, affordable, fast, and precise manner, helping clinicians detect cardiac sickness early and prevent cardiovascular disease deaths.Following the same steps for classifying heart sounds, the suggested RNN+LSTM approach deals with classifying heart murmurs as harmless (normal) and pathological (abnormal). The classification AUC for the optimal murmur classification combination was $\color {Orange}{\text{93}}{\text{.69}}\% $.
Quantification of Reliability in Object-Oriented Software: Functionality Perspective Anuj Kumar Yadav, C L P Gupta 2024 IEEE 1st Karachi Section Humanitarian Technology Conference Khi Htc 2024, 2024 Object-oriented design and development are now commonly used in software development environments. Important design concepts including inheritance, coupling, cohesion, and encapsulation are supported by this method. The work in progress aims at offering a functionally-based, robust estimating technique for object-oriented design. Functionality plays a critical part in producing high-quality products within predetermined time and cost limits. This paper introduces an approach for quantifying functionality and reliability, acknowledging the paramount importance of functionality during the design phase. Statistical analyses affirm functionality as a significant factor influencing software reliability. Building on the proposed mapping of object-oriented design features to functionality, a multiple regression equation has been constructed to calculate the functionality of design hierarchies. It is clear that usefulness has a favourable effect on object-oriented systems’ dependability. In order to determine dependability based on functionality, an additional multiple regression equation is created. Lastly, experimental trials are used to validate the suggested model.
Integrity Evaluation Model for Object Oriented Software: A Developer Perspective Vivek Gupta, C L P Gupta 2024 IEEE 1st Karachi Section Humanitarian Technology Conference Khi Htc 2024, 2024 Software integrity stands as a paramount element in software development. Evaluating integrity through design properties proves more fitting, and its validation underscores the genuine impact of structural and functional aspects in object-oriented design software. Integrity serves as a crucial quality indicator, and gauging it opens avenues for streamlining and enhancing the maintenance process. Software integrity offers valuable insights throughout the phases of design, coding, testing, and quality assurance. Both researchers and practitioners emphasize the desirability and significance of the integrity aspect in developing secure and robust software. Despite its vital role in the software development process, integrity is often inadequately managed. This paper underscores the necessity and significance of integrity during the design phase and endeavors to establish a substantial correlation between integrity and design properties. In light of this, a model is proposed for evaluating integrity in object-oriented design through the establishment of multiple linear regressions. Ultimately, the proposed model undergoes validation through experimental trials.
Deep Learning Application in PQRS Segment Detection in ECG Data Amit Kumar Pandey, C. L. P. Gupta 2024 International Conference on Electrical Electronics and Computing Technologies Iceect 2024, 2024 The electrocardiogram is a vital piece of information about a person's health that provides measurements of changes in the cardiovascular system. When using complex software modules to diagnose diseases, the clinical ECG signal is always tainted by power line interference and electromagnetic fields, leading to inaccurate results.Therefore, it's critical to reduce these ECG data acquisition recording inaccuracies in order to perform precise clinical analysis. We are working on an algorithm that will split the data array of three successive ECG signals into three equivalents. The Empirical Mode After decomposing the data matrix and running it through the Independent Component Generation algorithm, we will identify and remove any noisy components using deep learning scheme. To obtain an error-free result of ECG data, we will then apply reverse ICA and EMD features for deep learning.
Malicious Traffic Classification in WSN using Deep Learning Approaches Chhote Lal Prasad Gupta, Dinesh Rajassekharan, Dilip Kumar Sharma, Mohanraj Elangovan, Varatharaj Myilsamy, Kamal Upreti 2023 International Conference on Communication Security and Artificial Intelligence Iccsai 2023, 2023
