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With the increased use of automated systems, the Internet of Things (IoT), and sensors for real-time water quality monitoring, there is a greater requirement for the timely detection of unexpected values. Technical faults can introduce anomalies, and a large incoming data rate might make the manual detection of erroneous data difficult. This research introduces and applies a pioneering technology, Multivariate Multiple Convolutional Networks with Long Short-Term Memory (MCN-LSTM), to real-time water quality monitoring. MCN-LSTM is a cutting-edge deep learning technology designed to address the difficulty of detecting anomalies in complicated time series data, particularly in monitoring water quality in a real-world setting. The growing reliance on automated systems, the Internet of Things (IoT), and sensor networks for continuous water quality monitoring is driving the development and deployment of the MCN-LSTM approach. As these technologies become more widely used, the rapid and precise identification of unexpected or aberrant data points becomes critical. Technical difficulties, inherent noise, and a high data influx pose significant hurdles to manual anomaly detection processes. The MCN-LSTM technique takes advantage of deep learning by integrating Multiple Convolutional Networks and Long Short-Term Memory networks. This combination of approaches offers efficient and effective anomaly detection in multivariate time series data, allowing for identifying and flagging unexpected patterns or values that may signal water quality issues. Water quality data anomalies can have far-reaching repercussions, influencing future analyses and leading to incorrect judgments. Anomaly identification must be precise to avoid inaccurate findings and ensure the integrity of water quality tests. Extensive tests were carried out to validate the MCN-LSTM technique utilizing real-world information obtained from sensors installed in water quality monitoring scenarios. The results of these studies proved MCN-LSTM's outstanding efficacy, with an impressive accuracy rate of 92.3%. This high level of precision demonstrates the technique's capacity to discriminate between normal and abnormal data instances in real time. The MCN-LSTM technique is a big step forward in water quality monitoring. It can improve decision-making processes and reduce adverse outcomes caused by undetected abnormalities. This unique technique has significant promise for defending human health and maintaining the environment in an era of increased reliance on automated monitoring systems and IoT technology by contributing to the safety and sustainability of water supplies. [ABSTRACT FROM AUTHOR]

Coconut water is the clear liquid found inside coconuts, famous for rehydrating after exercise or while suffering from a minor sickness. The essential issue tackled in this paper is how to estimate the appropriate stage of maturity of coconut water, which is a time-consuming task in the beverage industry since, as the coconut age increases, the coconut water flavor varies. Accordingly, to handle this issue, an adaptive model based on Fuzzy Neural Network and Sperm Whale Optimization, dubbed FNN–SWO, is developed to assess coconut water maturity. The Sperm Whale Optimization (SWO) algorithm is a meta-heuristic optimization algorithm. It is embedded in this model along with neural networks and fuzzy techniques (FNN system), which can be employed as an essential building block in the beverage industry. The proposed FNN–SWO model is trained and tested utilizing fuzzy rules with an adaptive network. In contrast, the SWO algorithm is adopted to determine the optimal weights for the fuzzy rules. Three subsets of data divided according to three levels of coconut water maturity-tender, mature, and very mature, are used to validate the combined FNN–SWO model. Depending on these three subsets of data, a comparison of the proposed FNN–SWO model has been conducted against a set of the most common conventional techniques. These techniques include Support Vector Machine, Naïve Bayes, FNN, Artificial Neural Network, as well as their embedding with other meta-heuristic optimization algorithms. For various key performance indicators, such as recall, F1-score, specificity, and accuracy, the proposed FNN–SWO model provides the best prediction outcomes compared to the current time-consuming techniques. The dominance of the proposed FNN–SWO model is evident from the final findings compared to its time-consuming peers for estimating coconut water maturity on time. As a result, the proposed FNN–SWO model is an effective heuristic for locating optimal solutions to classification problems. It can thereby be reassuringly applicable to other similar prediction problems. Additionally, it would benefit the scientific community interested in evaluating coconut water. [ABSTRACT FROM AUTHOR]

• A modified Bee Colony Optimization algorithm for the Internet of Medical Things is proposed. • Two different scenarios are used to test the performance of the proposed algorithm. • Experiments showed the effectiveness of the proposed method. Internet of Medical Things (IoMT) is a recently introduced paradigm which has gained relevance as an emerging technology for widely connected and heterogeneous networks. In the medical context, these networks involve many different processes run by different types of devices called objects that interact and collaborate to achieve a common goal (e.g, diagnosis, treatment, monitoring, or rehabilitation of a patient). An IoMT framework in a smart healthcare system dynamically monitors patients to respond to their assistance demands so that vital signs of critical or unusual cases can be uncovered based on the collected data. To this end, an effective technique called SIoMT (Swarm Intelligence optimization technique for the IoMT) is proposed in this paper for periodically discovering, clustering, analyzing, and managing useful data about potential patients. Notably, the SIoMT technique is widely used with distributed nodes for analyzing and managing data groups. Different from the existing clustering algorithms, SIoMT performs clustering based on the characteristics and distance between objects or swarms. More specifically, these data are collected and grouped, in early stage, using a clustering approach inspired by the Bee Colony Optimization algorithm (BCO), adopting some standard quality measures which helped minimizing the latency and required computational cost. To test the performance of the proposed SIoMT, one public dataset (Ward2ICU) was considered from the online source. Various experiments were done to analyze the effects of different parameters on the proposed SIoMT's performance, and the results from the final variant of the proposed algorithm were compared against different variants of the same algorithm with different clustering algorithms and different optimization algorithms. Subsequently, after analyzing different components by solving various IoMT datasets, the capability and the superiority of the proposed SIoMT approach is well-established among its competitive counterparts. [ABSTRACT FROM AUTHOR]