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Digital image processing is the use of a digital computer to process digital images. Image processing transforms input images into digital form for certain operations to obtain useful information. Segmentation is a well-known process used in image processing that partitions input images into different regions. Image segmentation is a sub-area of computer vision and digital image processing for grouping similar segments of an image under respective class labels. Several methods were performed with neutrosophic sets on dissimilar image-processing domains. However, the denoising and segmentation were not carried out accurately with minimal time complexity. To address these issues, many image segmentation methods are reviewed.

Early recognition and classification of brain tumors are significant areas in medical imaging that aids for providing mainly appropriate surgery to store patients' lives. Conventional machine learning techniques face significant challenges in achieving accurate tumor detection within minimal time consumption. Therefore, Neutrosophic Morphological Segmented Gaussian Regressive Deep Convolutional Network (NMSGRDCN) method is developed for efficient brain tumor detection with minimal time consumption. Medical brain MRI images serve as input and are transmitted to the input layer. An input medical image is sent to convolutional layer, wherever image denoising process is carried out using the Levenberg–Marquardt minimum variance smoothing filter to remove noisy artifacts. Subsequently, the preprocessed medical image is transmit to max pooling layer, wherever Neutrosophic Hamann Indexive Morphological Pixel Image Segmentation Process is carried out to divide the preprocessed image to segments and extract ROI. Finally, segmented image is forwarded to fully connected layer. Gaussian process regression is employed to analyze the extracted features, and binary step activation is used to categorize images as normal or tumor. The classified outcomes are attained at output layer. Experimental assessment is conducted using medical images, considering various metrics such as PSNR, disease diagnosis accuracy, precision, recall, F1 score, diagnosis time. Performance result shows that the NMSGRDCN technique improves data accuracy of brain tumor disease detection with reduced processing time compared to conventional methods. [ABSTRACT FROM AUTHOR]

Neutrosophic sets (NS) have referred to as interval fuzzy sets applied in minimizing the uncertainty and fuzziness in computer-vision and machine-learning communities and hence employed for several applications. As far as medical image processing applications are concerned NSs are obtained as an important technique for de-noising. Also, fuzzy segmentation with machine and deep learning is determined as a familiar procedure that splits input image into distinct regions for precise learning. Several research works conducted in different image-processing domains. However, less works was focused on denoising and segmentation of medical image processing with minimal time complexity and accuracy. In this work we plan to develop a Kalman--Bucy Filtered Neutrosophic Neuro Fuzzy Image Denoising (KBF-NNFID) method with the objective of reducing the noisy artifacts with higher peak signal-to-noise ratio in a computationally efficient manner. First, medical images obtained from Brain MRI LGG segmentation dataset are subjected to filtering employing Kalman Bucy Filtering algorithm with series of measurements examined. Second with the filtered medical images provided as input, uncertainty is handled by utilizing Neutrosophic Neuro Fuzzy set (NNFS) with help of the membership grade. With the aid of three membership grades, i.e., truth, indeterminacy and falsity, uncertainty involved in noisy image are said to be handled in a time efficient manner. By this way, an efficient image denoising process is performed with better PSNR. Experimental evaluation is carried out using medical images with different performance metrics such as enhanced PSNR and true positive rate up to 13%, 14% as well minimum execution time by 38% using medical images. [ABSTRACT FROM AUTHOR]

During major earthquakes, existing buildings have collapsed or suffered serious damage, resulting in number of losses, severe injuries, and deaths. Based on literature, the influence of this work reviewed the effects of interface with different materials and also to find how infilled frames behave in framed structure. This study's primary goal is to strengthen RC-framed structures and increase the ductility of infilled frames by using interface materials. The research offers a full range and points relevant to ductile parameters for more results in the field of infilled frames using interface materials. In parametric investigation the interface material with interface thickness and the combination of interface material with a particular frame, from that optimum value to be identified. This research benefits researchers, professionals, and specialists the behaviour of various structural systems, as well as innovative mitigation techniques that have been used in the literature to build progressive collapse resistance experimentally.

This paper explores Speech Emotion Recognition (SER), crucial for applications like education, customer service, and healthcare. SER aims to predict emotions conveyed through speech, utilizing various frameworks accurately. The paper evaluates existing methods based on feature extraction and classification, highlighting their strengths, limitations, and overall accuracy. By analyzing these parameters, the paper provides insights into the effectiveness of current SER approaches. This outline is an important asset for scientists and specialists looking to comprehend and further develop feeling acknowledgment frameworks in true settings. [ABSTRACT FROM AUTHOR]