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Maneesha

Publications and source records attributed to Maneesha.

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Developing Assessment Methods for Evaluating Learning Experience

This research aims to investigate the gender-based learning experiences of engineering students enrolled in the Probability and Statistics course, focusing on the four different assessment methods employed namely direct conceptual learning (DCL), symposium, applied deployment and collaborative learning. The study encompasses 299 engineering students, comprising 90 females and 209 males. Multivariate Analysis of Variance (MANOVA), is used to gain deeper insights into the complex interplay between assessment methods and their influence on student learning. The results of the statistical analysis reveal that there are significant differences in the learning outcomes between female and male engineering students in the assessment methods of direct conceptual learning, symposium, and applied deployment. The findings suggest that there is no significant difference in the learning outcomes between female and male engineering students in the collaborative learning assessment method. The graphical representation visually confirms the significant differences in direct conceptual learning, symposium, and applied deployment, while illustrating no significant difference in collaborative learning between female and male engineering students.

stat.OT

Privacy Challenges In Image Processing Applications

As image processing systems proliferate, privacy concerns intensify given the sensitive personal information contained in images. This paper examines privacy challenges in image processing and surveys emerging privacy-preserving techniques including differential privacy, secure multiparty computation, homomorphic encryption, and anonymization. Key applications with heightened privacy risks include healthcare, where medical images contain patient health data, and surveillance systems that can enable unwarranted tracking. Differential privacy offers rigorous privacy guarantees by injecting controlled noise, while MPC facilitates collaborative analytics without exposing raw data inputs. Homomorphic encryption enables computations on encrypted data and anonymization directly removes identifying elements. However, balancing privacy protections and utility remains an open challenge. Promising future directions identified include quantum-resilient cryptography, federated learning, dedicated hardware, and conceptual innovations like privacy by design. Ultimately, a holistic effort combining technological innovations, ethical considerations, and policy frameworks is necessary to uphold the fundamental right to privacy as image processing capabilities continue advancing rapidly.

cs.CR