Heavy metal measurement is vital for ecological risk assessment and regulatory compliance. This study reports a sensor using gold nanoparticle-modified carbon thread electrodes for the simultaneous detection of Cd²⁺, Pb²⁺, Cu²⁺, and Hg²⁺ in water samples. Differential pulse voltammetry (DPV) was employed, achieving detection limits of 0.99 µM, 0.62 µM, 1.38 µM, and 0.72 µM, respectively, with a linear span of 1–100 µM. The sensor operated effectively in acidic conditions, with excellent selectivity, repeatability, and reproducibility. Real water samples from various lakes in Hyderabad, India, were analyzed to validate their practical application. To extract the sensing features a convolutional neural network (CNN) model was used to process DPV signals, enhancing heavy metal ion classification with high accuracy. Performance metrics such as precision, recall, and F1 score were evaluated. Integration with IoT technology has improved the user experience, advanced heavy metal quantification capabilities, and further enabled remote monitoring.

The heart valve ailments (HVAs) are due to the defects in the valves of the heart and if untreated may cause heart failure, clots, and even sudden cardiac death. Automated early detection of HVAs is necessary in the hospitals for proper diagnosis of pathological cases, to provide timely treatment, and to reduce the mortality rate. The heart valve abnormalities will alter the heart sound and murmurs which can be faithfully captured by phonocardiogram (PCG) recordings. In this paper, a time‐frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals. Spline kernel‐based Chirplet transform (SCT) is used to evaluate the time‐frequency representation of PCG recording, and the features like L1‐norm (LN), sample entropy (SEN), and permutation entropy (PEN) are extracted from the different frequency components of the time‐frequency representation of PCG recording. The DLKSRN formulated using the hidden layers of extreme learning machine‐ (ELM‐) autoencoders and kernel sparse representation (KSR) is used for the classification of PCG recordings as normal, and pathology cases such as mitral valve prolapse (MVP), mitral regurgitation (MR), aortic stenosis (AS), and mitral stenosis (MS). The proposed approach has been evaluated using PCG recordings from both public and private databases, and the results demonstrated that an average sensitivity of 100%, 97.51%, 99.00%, 98.72%, and 99.13% are obtained for normal, MVP, MR, AS, and MS cases using the hold‐out cross‐validation (CV) method. The proposed approach is applicable for the Internet of Things‐ (IoT‐) driven smart healthcare system for the accurate detection of HVAs.

The phonocardiogram (PCG) signal deciphers the mechanical activity of the heart, and it consists of the fundamental heart sounds (S1 and S2), murmurs, and other associated sounds (S3 and S4). Detection of fundamental heart sound activity (FHSA) is vital for the automated analysis of PCG signals to diagnose various heart valve diseases. This paper proposes a time-frequency domain (TFD) deep neural network (DNN) approach for automated FHSA detection using PCG signals. The modified Gaussian window-based Stockwell Transform (MGWST) is used to obtain the time-frequency representation (TFR) of PCG signals. The Shannon-Teager-Kaiser Energy (STKE), smoothing, and thresholding techniques are then employed to evaluate the segmented heart sound components. The TFD Shannon entropy (TFDSE) features are computed from the segmented heart sound components of the PCG signal. The deep neural network (DNN) developed based on the stacked autoencoders (SAEs) is used for automated identification of FHSA components. The performance of the proposed approach is evaluated using two publicly available standard databases (Database 1: Michigan heart sound and murmur database and Database 2: PhysioNet Computing in Cardiology Challenge 2016). The results demonstrate that the proposed approach has achieved the accuracy, sensitivity, specificity, and precision values of 99.55 %, 99.93 %, 99.26 %, 99.02 % for Database 1, and 95.43 %, 97.92 %, 98.32 %, 97.60 % for Database 2, respectively. It is shown that, the proposed FHSA detection approach has obtained better accuracy than existing methods.