Polycystic Ovarian Disease (PCOD), also known as Polycystic Ovary Syndrome (PCOS), is a prevalent hormonal and metabolic condition primarily affecting women of reproductive age worldwide. It is typically marked by disrupted ovulation, an increase in circulating androgen hormones, and the presence of multiple small ovarian follicles, which collectively result in menstrual irregularities, infertility challenges, and associated metabolic disturbances. This study presents an automated diagnostic framework for PCOD detection from transvaginal ultrasound images, leveraging an Enhanced \(\mathrm {EfficientNet\text {-}B3}\) convolutional neural network architecture. The model incorporates attention mechanisms, batch normalization, and dropout regularization to improve feature learning and generalization. Bayesian Optimization was employed to fine-tune critical hyperparameters, including learning rate, batch size, and dropout rate, ensuring optimal model performance. The proposed system was trained and validated on a curated ovarian ultrasound image dataset, applying data augmentation and SMOTE techniques to address class imbalance. Experimental evaluation demonstrated that the Enhanced \(\mathrm {EfficientNet\text {-}B3}\) model achieved a classification accuracy of 94.8%, sensitivity of 93.2%, specificity of 95.5%, precision of 94.0%, and an F1-score of 93.6% on the independent test set. Interpretability was enhanced through Grad-CAM visualization, which effectively localized diagnostically significant regions within the ultrasound images, corroborating clinical findings. These results highlight the potential of the proposed deep learning-based framework to serve as a reliable, scalable, and interpretable decision-support tool for PCOD diagnosis, offering improved diagnostic consistency and reducing operator dependency in clinical workflows.

Human Activity Recognition (HAR) is a vital technology in domains such as healthcare, fitness, and smart environments. This paper presents an innovative HAR system that leverages machine-learning algorithms deployed on the B-L475E-IOT01A Discovery Kit, a highly efficient microcontroller platform designed for low-power, real-time applications. The system utilizes wearable sensors (accelerometers and gyroscopes) integrated with the kit to enable seamless data acquisition and processing. Our model achieves outstanding performance in classifying dynamic activities, including walking, walking upstairs, and walking downstairs, with high precision and recall, demonstrating its reliability and robustness. However, distinguishing between static activities, such as sitting and standing, remains a challenge, with the model showing a lower recall for sitting due to subtle postural differences. To address these limitations, we implement advanced feature extraction, data augmentation, and sensor fusion techniques, which significantly improve classification accuracy. The ease of use of the B-L475E-IOT01A kit allows for real-time activity classification, validated through the Tera Term interface, making the system ideal for practical applications in wearable devices and embedded systems. The novelty of our approach lies in the seamless integration of real-time processing capabilities with advanced machine-learning techniques, providing immediate, actionable insights. With an overall classification accuracy of 90%, this system demonstrates great potential for deployment in health monitoring, fitness tracking, and eldercare applications. Future work will focus on enhancing the system’s performance in distinguishing static activities and broadening its real-world applicability.

Watermarking is a technology that complements cryptography by embedding imperceptible signals in work. Information hiding techniques have recently become important in a number of application areas. Digital audio, video and images are increasingly furnished with distinguishing but imperceptible marks, which may contain a hidden copyright notice that helps to prevent unauthorized copying directly. One approach is to introduce an invisible image known as digital watermark, into an image sequence. This paper presents secured algorithms for embedding digital watermarks into images. The proposed method performs imperceptible watermarking of images in frequency domain. The watermark is embedded in the DWT and DCT domain of an image in a multi-resolution way. In the decoding phase, once the watermark is extracted from the watermarked image, certain performance measures such as peak signal to mean noise ratio (PSNR) and correlation are calculated. Different types of attacks have been applied to the watermarked image to test the robustness of the applied technique and for each case, PSNR and correlation are calculated. Even though DWT method gives better results when compared to DCT, performances of both methods are good.

This article explores the Internet of Things (IoT), an innovative technical advancement that utilizes the capabilities of billions of sensors across various applications. Sensors are critical to the IoT environment as they collect crucial data for complex analysis. The emergence of the Internet of Things (IoT) and its accompanying sensor technology has significant implications for various fields, including smart urban planning, advanced agriculture, online education, and healthcare. The Internet of Things (IoT) has played a crucial role in tackling worldwide health challenges, notably in the healthcare sector, with a particular emphasis on the recent COVID-19 pandemic. The epidemic has heightened the need for digital and home-based healthcare solutions. The Internet of Things (IoT) enhances healthcare services by providing precise patient monitoring over a unified digital network. This article examines the various uses, technical intricacies, and difficulties that exist in the healthcare field. A thorough investigation was conducted using reputable databases such as Google Scholar, Elsevier, PubMed, ACM, ResearchGate, Scopus, and Springer. Relevant keywords directed the search. The narrative highlights and emphasizes the significant impact of IoT on healthcare, explicitly identifying prospective research areas for doctors, scholars, and researchers to overcome obstacles in the field. As anticipated, the Internet of Things (IoT) serves as a guiding light for improved healthcare delivery. The Survey demonstrates that combining IoT with cutting-edge technology enhances computing capacities, emphasizing IoT’s widespread, advantageous, and extensive nature. To summarise, this discussion examines future difficulties and provides valuable solutions to strengthen the healthcare infrastructure led by the Internet of Things (IoT) during the COVID-19 crisis and future health catastrophes.

Ultra-wide Band (UWB) technology has emerged as a pivotal tool for human motion detection, finding applications in diverse areas ranging from smart homes to automotive safety. This paper presents a comprehensive survey of methodologies employed in UWB-based motion detection, elucidating their strengths, challenges, and performance metrics. While several methods, including Convolutional Neural Network (CNN) approaches, have been explored, challenges such as motion state overlaps, the necessity for enhanced spatial resolution, and background noise interference persist. Among the various methods analyzed, the SGWO-based RMDL technique emerges as a frontrunner, offering superior accuracy, reduced mean squared error, and impressive true negative and positive rates. Moreover, its computational efficiency sets a precedent in human motion detection. This paper provides insights into the state-of-the-art Through the wall imaging and human vital signs observation for future research and realtime applications.