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Menopause is an obligatory phenomenon in a woman’s life. Some women face mental and physical issues during their menopausal period. Depression is one of the issues some women struggle with during their menopausal period. The scarcity of specialists, lack of knowledge, and awareness is the motivating factor in this research to predict depression among menopausal women and enhance their quality of life. The prediction of depression symptoms among menopausal women with machine learning techniques is promising and challenging in artificial intelligence. This study develops a system with significant accuracy using a supervised machine-learning approach. Various classification algorithms are used to determine the best-performing classifier by evaluating multiple parameters, including accuracy, sensitivity, specificity, precision, recall, F-Measure, Receiver Operating Characteristic (ROC), Precision–Recall​ Curve (PRC), and Area Under the Curve (AUC). We found that Random Forest and XGBoost classifiers are the performers with 99.04% accuracy employing the 14 most significant features.

Background: AI, IoT, and BIM technologies are revolutionizing the building business. This study applies these methods to the multi-stage categorization of construction site modeling items to create an "evolutionary" digital twin. Objective: Develop and test a BIM-AI method integrating CNN and FFNN architectures. The goals are BIM project identification, classification, and assessment throughout their life cycle. Methods: The approach uses moving cameras for picture modeling and IoT integration. Augmented reality and big data technologies explore the dynamic transformation of actual building structures into BIM representations. An AI system that analyzes construction site modeling items using CNN and FFNN is a major part of the study. Results: The article highlights the usefulness of site conformity detection during BIM model building, displaying consistency and quantifying ongoing operations. The study shows that scaling point cloud and mesh models and optimizing the "evolutionary" BIM project of the building site's digital twin are promising. Conclusion: The findings of this study provide important insights and improve BIM modeling techniques, notably in creating a multi-stage "evolutionary" digital twin of the building site. This pioneering construction site modeling using BIM and AI opens the door to future developments and improvements.

Background: Image processing is crucial in different scientific fields and is at the core of digital technology owing to its diverse uses. Due to the extensive usage of digital image processing, user-friendly, efficient picture processing and editing software is needed. Due to the popularity of commercial software like Microsoft's Digital Image Suite and Adobe Photoshop, similar alternatives have emerged. Objective: This project aims to create a strong and adaptable picture editor utilizing MATLAB capabilities with an intuitive GUI for users of varied competence levels. The article seeks to provide a viable alternative to commercial image processing software that is simple to use and has many features. Methods: MATLAB's image processing tools and unique methodologies created an image editor with extensive capabilities. The editor's development concentrated on supporting RGB, monochrome, and binary image formats and allowing users to manipulate them. The software's layout, design, message, usability, and cultural appropriateness were considered to improve user experience. Results: The image editor effectively enhances digital picture quality for medical image restoration and satellite image reconstruction. The program is user-friendly due to its minimal download size, easy setup, and straightforward UI. It shows how digital technology evolves, offering a flexible tool for beginners and experts. Conclusion: This study successfully developed a user-friendly MATLAB-based picture editor for varied digital image processing demands. The tool improves data analysis success and accuracy in engineering, research, and finance due to its simplicity and powerful features.