Supreme Knowledge Foundation Group of Institutions

Image denoising remains a fundamental challenge in low-level vision, particularly in scenarios where noise is nonuniform, structure-dependent, or difficult to model statistically. This paper presents a novel denoising framework that integrates density-based clustering with intuitionistic fuzzy logic to achieve robust detection and suppression of noise pixels. The proposed method first constructs local feature descriptors for every pixel and employs the OPTICS clustering algorithm to identify density anomalies that correspond to potential noise points. For each detected candidate, an intuitionistic fuzzy model computes membership, non-membership, and hesitation degrees based on both local similarity and deviation measures. A pixel-adaptive restoration rule then selectively replaces corrupted pixels using median filtering while preserving uncorrupted structures. Experimental results validate that the proposed OPTICS-fuzzy approach efficiently eliminates noise while maintaining fine image specifics, outperforming conventional clustering-based and fuzzy-based denoising methods across multiple benchmark datasets. The method exhibits strong robustness to varying noise levels and nonGaussian noise distributions, making it suitable for real-world imaging applications.

This work reports the design, fabrication, and comprehensive characterization of a matrix-coupled optoelectronic resistor array based on white InGaN light-emitting diodes (LEDs) and CdS photoresistors, developed as a tunable electronic material platform for wide-range analog resistance control. The device incorporates an M × N (4 × 4 demonstrated) LED–photoresistor matrix housed within a mirror-coated polymeric spacer, enabling distance-dependent optical coupling that governs the electronic response of the CdS detectors. Detailed materials processing steps—including polylactic acid-based spacer fabrication, protected-silver reflective coatings, hot-melt encapsulation, and black nitrocellulose optical isolation—are presented to highlight the role of structural and interfacial materials in optical confinement and thermal stability. The optical–electronic behavior is described through an analytical model integrating a modified Shockley LED law, power-law radiance scaling, and Beer–Lambert attenuation through the spacer medium. Model parameters were extracted from measured LED I–V characteristics and fitted light-dependent resistor (LDR) resistance–voltage datasets, achieving excellent agreement with experiments (typical pointwise error < ± 3

A dangerous kind of skin cancer that may develop anywhere on the body is melanoma. Early identification of melanoma lesions greatly improves the likelihood of successful therapy. In picture segmentation, learning-based segmentation techniques have recently surpassed conventional algorithms. A deep learning-based classification and Triangular Intuitionistic fuzzy-based segmentation framework is introduced in this paper to enhance the identification and categorization of malignant skin lesions. The Skin Lesion Analysis toward Melanoma Detection Challenge dataset is used to test the framework. In order to segment and detect lesions in real time, the approach consists of two primary processes. Preprocessing involves K-Medoid Clustering-based noise detection followed by Fuzzy logic-oriented noise removal to enhance image quality and remove unwanted artifacts. After preprocessing, skin lesions are precisely localized using You-Only-Look-Once edition 8 (YOLOv8). There are two steps in the segmentation process to determine the affected areas. Phase I computes the smallest spanning tree for identifying impacted zones based on threshold values using a graph-based framework. In the phase II, two segmentation approaches — Triangular Intuitionistic Fuzzy Numbers (TIFNs) and Triangular Dense Neutrosophic Numbers (TDNNs) — are comparatively evaluated. The comparative analysis demonstrates that TDNNs offer superior performance, yielding more accurate and reliable segmentation results. The [Formula: see text] photos used in the experiments are from three public datasets: PH2, ISBI [Formula: see text], and ISIC [Formula: see text]. With a Jac score of [Formula: see text] on the ISIC [Formula: see text] dataset and [Formula: see text] accuracy on the ISBI [Formula: see text] and [Formula: see text] PH2 datasets, the findings were encouraging. The suggested strategy performed marginally better than current frameworks with predetermined settings in the majority of circumstances.

Maternal health issues pose significant risks to pregnant women, often leading to complications arising from conditions such as diabetes or abnormal glucose levels, depression, hypertension, anxiety, and other disorders. Early identification and monitoring of risk factors are crucial to minimizing such complications. This study leverages real-world data to identify and predict maternal health risks using a machine learning (ML)-based system designed to forecast the likelihood of maternal illness. Multiple ML models were integrated into the system to enhance the accuracy of the prediction. A dataset collected from maternity hospitals and clinics was subjected to four different training and testing scenarios. Exploratory data analysis revealed hypertension, hypotension, and diabetes as the primary contributors to complications. The proposed methodology introduced a novel approach to addressing high-risk factors, emphasizing class-specific performance to better distinguish between low, medium, and high-risk cases. Among the models, using the random forest classifier, we have achieved exceptional performance, delivering a success rate of 91

Here, a full-wave voltage-doubler in conjunction with a lumped LC matching network has been thoroughly examined and found to be a superior option for the rectifier section of the antenna. This can be used to harvest DC power from the freely available wild RF energy in the surrounding environment while on the move. Following a number of diodes’ auditions, HSMS-285B Schottky diodes were ultimately selected as the best options for rectification due to its great sensitivity to extremely low-input power, fast-operating speed, lack of a turn-on voltage requirement, and low loss at high frequencies. The matching network’s lumped parts and the full-wave rectifier’s charging capacitors were carefully optimized and tuned, and the result was an encouraging maximum overall rectifier efficiency of 49.5