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Museum contents are vulnerable to bad ambience conditions and human vandalization. Preserving the contents of museums is a duty towards humanity. In this paper, we develop an Internet of Things (IoT)-based system for museum monitoring and control. The developed system does not only autonomously set the museum ambience to levels that preserve the health of the artifacts and provide alarms upon intended or unintended vandalization attempts, but also allows for remote ambience control through authorized Internet-enabled devices. A key differentiating aspect of the proposed system is the use of always-on and power-hungry sensors for comprehensive and precise museum monitoring, while being powered by harvesting the Radio Frequency (RF) energy freely available within the museum. This contrasts with technologies proposed in the literature, which use RF energy harvesting to power simple IoT sensing devices. We use rectenna arrays that collect RF energy and convert it to electric power to prolong the lifetime of the sensor nodes. Another important feature of the proposed system is the use of deep learning to find daily trends in the collected environment data. Accordingly, the museum ambience is further optimized, and the system becomes more resilient to faults in the sensed data.

Monitoring water quality is an important challenge in both developed and developing countries. Remote sensing data can form a highly frequent dataset with acceptable spatial coverage that can be used to remotely monitor water quality. This paper presents a novel automated model for remotely monitoring water quality to address the problem of insufficient samples and save the time and cost of sample collection. The proposed model estimates both optical and non-optical water quality parameters via Sentinel-2A data. A bio-inspired hybrid model of a Binary Whale Optimization Algorithm (BWOA) and Artificial Neural Network (ANN) (BWOA-ANN) is applied to determine the relationship between extracted reflectance values from Sentinel-2A images and analyzed samples. The novelty of this model is to solve two main problems of remote water quality monitoring: poor applicability and low non-optical parameter estimation accuracy. For the first problem, a proposed fully automated model with band selection using the BWOA to automatically select the optimal features (Sentinel-2A bands) that are suitable for each water quality parameter. The second problem is addressed by automatically detecting the relationship between non-optical parameters, such as the total phosphorus, and optical parameters, such as chlorophyll-a. Three datasets with different locations, seasons, and parameters were selected to test the proposed BWOA-ANN. The experimental results demonstrated good regression with a mean R 2 value of 0.916 for optical parameters and 0.890 for non-optical parameters. The proposed model was found to outperform the ANN with an R 2 value higher by 40% and 52% for the optical and non-optical parameters, respectively.

Parkinson's disease (PD) is the second most common neurodegenerative disorder disease right after Alzheimer's and the most common movement disorder for elderly people. It is characterized as a progressive loss of muscle control, which leads to trembling characterized by uncontrollable shaking, or (tremors) in different parts of the body. In recent years, deep learning (DL) models achieved significant progress in automatic speech recognition, however, limited studies addressed the problem of distinguishing people with PD for further clinical diagnosis. In this paper, an approach for the early detection of patients with PD using speech features was proposed, a recurrent neural network (RNN) with long short-term memory (LSTM) is applied with the batch normalization layer and adaptive moment estimation (ADAM) optimization algorithm used after the network hidden layers to improve the classification performance. The proposed approach is applied with 2 benchmark datasets of speech features for patients with PD and healthy control subjects. The proposed approach achieved an accuracy of 95.8% and MCC=92.04% for the testing dataset. In future work, we aim to increase the voice features that will be worked on and consider using handwriting kinematic features.