ABSTRACT: Digital watermarking has been extensively employed to ensure copyrighting of images and authenticity, but in some cases, it becomes necessary to remove watermarks such as in restoration of old archives, in forensic investigations, or in the reuse of images in a research database. Conventional watermark removal methods tend to destroy the clarity and structure of the image resulting in conspicuous distortions or blurring. In order to rectify these shortcomings, this paper proposes an efficient watermark removal structure grounded on IMPRINTS (Image Processing and Restoration Techniques). The suggested solution uses superior inpainting algorithms to recreate the hidden image areas following watermark erase and retains the texture, color consistency, and edge continuity. The scheme involves steps of preprocessing, detection of watermarks, mask generation and repair to produce minimum visual artifacts. Experimental tests performed with various watermarked image datasets demonstrate that IMPRINTS outperforms the traditional interpolation and CNN-based watermarking in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The findings depict that the IMPRINTS process is effective in removing watermarks of high quality in visual images and structural integrity, thus, it is a dependable process in image restoration procedures in the real world.
Keywords: Image Restoration, IMPRINTS, Watermark Removal, Image Inpainting, Edge Detection, PSNR, SSIM, Digital Image processing.

ABSTRACT: Cognitive ability plays a key role in a student’s growth, influencing how well they can think, remember, calculate, and understand information. This framework introduces a smart semantic chatbot designed to evaluate students' cognitive abilities in a more efficient and personalized way. It automatically gathers student responses through Google Forms, where the questions are thoughtfully grouped into four key areas: Logical Reasoning, Memory & Attention, Mathematical Skills, and Verbal Reasoning. Responses are stored in Google Sheets and fetched dynamically using an API for processing. The scoring system gives points for correct answers (10), medium answers (5), and incorrect ones (0), allowing for a detailed assessment in each domain. After assessing scores, the chatbot provides personalized feedback, identifying strengths and domains to improve, all without repeating itself. Unlike traditional tests, this system shows results in a clear table, helping students easily understand how they did. This proposed framework fills the gap in education by providing a simple, fast, and meaningful way to assess cognitive abilities, with an easy-to-use chatbot. A built-in recommender system further enhances the framework by analyzing students’ scores across the four domains and generating targeted suggestions. Based on performance patterns, it identifies areas where students are lacking, whether in logical reasoning, memory and attention, mathematical skills, or verbal reasoning, and provides specific recommendations for improvement in those weaker domains using association rule mining.
Keywords: Cognitive Ability Assessment, Semantic Chatbot, Student Performance Analysis, Feedback, Recommender System, Association Rule Mining.

ABSTRACT: This paper presents an innovative technology called the "smart shoe," designed to assist people with visual or hearing impairments in navigating their surroundings more easily. Equipped with advanced sensors and mini-computers, the shoe can detect uneven surfaces or obstacles in the wearer’s path. It provides feedback through vibrations or sound alerts to help guide them. Developed with ease of use and practicality in mind, this device aims to empower users, fostering greater confidence and independence. The goal of this technology is to create a more inclusive and secure environment for those with sensory challenges.
Keywords: Assistive Technology, Smart Wearables, Obstacle Detection, Accessibility & Inclusive Mobility.