Imaging examinations, such as chest X-ray (CXR) images, can be used for early detection of diseases inside the human body, such as lung cancer, and cardio-pulmonary-related diseases; however, they may contain patients’ private information, which raises concerns about the security levels of medical images in Internet of Medical Things (IoMT) systems. The promising authorization protocol algorithm in 5th-generation networking technology can protect medical image confidentiality, recoverability, and availability. Hence, we propose a symmetric cryptography protocol based on the Riemann–Lebesgue (R–L)-based key generator and a machine learning (ML)-based cryptography scheme for CXR image infosecurity. The R–L-based key generator uses the composite function, ${g}$ ( ${x}$ ) • sin((1/2 $+\,\,{N}$ ) • ${x}$ ), to dynamically produce the oscillation function by controlling the terms of the nonlinear function ${g}$ ( ${x}$ ), and frequency ${N}$ , which are used to produce the random- number seed and to select nonordered numbers and nonrepeated 256 secret keys in the key space. The ML-based scheme uses these random secret keys to train an image encryptor and decryptor, respectively. Based on the diffusion method, the ML-based cryptography scheme is used to change image pixel values. For evaluation experiments, CXR images are collected from the National Institutes of Health CXR database and used to evaluate image quality after encryption and decryption processes. For different cardiopulmonary-related disease images, the number of pixel change rates (NPCRs) and unified averaged changed intensity (UACI) are used to evaluate the security level and decryption quality. The experimental results show an average NPCR of 99.6% and average UACI of 32.61% between plain and cipher images for evaluating the security level, enabling further online diagnosis applications in IoMT systems.

Infiltration and blood leakage are serious life-threatening complications during intravenous administration and dialysis therapy. Serious infiltrations, such as drips and drugs, may cause inflection, sepsis, and extent of tissue damage. Blood leakage and blood loss are serious complications, as it takes only a few minutes to lose over 40% of adult blood volume, potentially resulting in mortality. Therefore, we propose integrating the flexible sensors and bidirectional hetero-associative memory (BHAM) network into a cloud computing-based warning tool for the detection of infiltration and blood leakage. The flexible sensors are arranged in two array patterns for liquid leakage detection and are fabricated on a soft substrate via a screen-printing technique. The BHAM network constructs a virtual alarm unit in an embedded system or tablet PC. This early warning unit can be employed to identify risk levels via a wireless network and cloud computing, and then indicates warning information on a remote monitor device or a mobile device. The proposed detection model has been validated experimentally, and the experimental results demonstrate its feasibility.

Human speech signals may contain specific information regarding a speaker's characteristics, and these signals can be very useful in applications involving interactive voice response (IVR) and automatic speech recognition (ASR). For IVR and ASR applications, speaker classification into different ages and gender groups can be applied in human–machine interaction or computer‐based interaction systems for customised advertisement, translation (text generation), machine dialog systems, or self‐service applications. Hence, an IVR‐based system dictates that ASR should function through users' voices (specific voice‐frequency bands) to identify customers' age and gender and interact with a host system. In the present study, we intended to combine a pitch detection (PD)‐based extractor and a voice classifier for gender identification. The Yet Another Algorithm for Pitch Tracking (YAAPT)‐based PD method was designed to extract the voice fundamental frequency (F ₀ ) from non‐stationary speaker's voice signals, allowing us to achieve gender identification, by distinguishing differences in F ₀ between adult females and males, and classify voices into adult and children groups. Then, in vowel voice signal classification, a one‐dimensional (1D) convolutional neural network (CNN), consisted of a multi‐round 1D kernel convolutional layer, a 1D pooling process, and a vowel classifier that could preliminary divide feature patterns into three level ranges of F ₀ , including adult and children groups. Consequently, a classifier was used in the classification layer to identify the speakers' gender. The proposed PD‐based extractor and voice classifier could reduce complexity and improve classification efficiency. Acoustic datasets were selected from the Hillenbrand database for experimental tests on 12 vowels classifications, and K‐fold cross‐validations were performed. The experimental results demonstrated that our approach is a very promising method to quantify the proposed classifier's performance in terms of recall (%), precision (%), accuracy (%), and F1 score.

Digital images can be easily shared or stored using different imaging devices, storage tools, and computer networks or wireless communication systems. However, these digital images, such as headshots or medical images, may contain private information. Hence, to protect the confidentiality, reliability, and availability of digital images on online processing applications, it is crucial to increase the infosecurity of these images. Therefore, an authorization encryption scheme should ensure a high security level of digital images. The present study aimed to establish a multilayer convolutional processing network (MCPN)-based cryptography mechanism for performing two-round image encryption and decryption processes. In the MCPN layer, two-dimensional (2D) spatial convolutional operations were used to extract the image features and perform scramble operations from grayscale to gray gradient values for the first-image encryption and second-image decryption processes, respectively. In the MCPN weighted network, a sine-power chaotic map (SPCM)-based key generator was used to dynamically produce the non-ordered pseudorandom numbers to set the network-weighted values as secret keys in a sufficiently large key space. It performs the second and first encryption processes using the diffusion method, modifying the image pixel values. Children’s headshots and medical images were used to evaluate the security level by comparing the plain and cipher images using the information entropy, number of pixel change rate, and unified averaged changed intensity indices. Moreover, the plain and decrypted images were compared to verify the decrypted image quality using the structural similarity index measurement and peak signal-to-noise ratio.

Automated meter reading systems (AMRS) are employed to measure and record electricity consumption data in smart grids, facilitating the construction of various consumption models for residential, commercial, and industrial electricity users. These standard electricity-consumption models (SECM), along with their associated consumption patterns, can be further used for applications in load forecasting, technical loss (TL) analysis, and non-technical loss (NTL) detection. Therefore, this study applies the SECM to NTL detection by proposed novel screening model that combines the endpoint detection (EPD) and short-time Fourier transform (STFT) with a one-dimensional (1D) convolutional neural network (1D CNN) based classifier. The EPD method is employed to preliminarily detect discrepancies between metering data and historical records, focusing on the time-domain variations in electricity consumption. Subsequently, the STFT is applied to analyze the frequency contains in the drastically changing time-domain data and then generates the visualization color feature patterns. With theses feature patterns, the 1D-CNN based classifier is used to identify the data into normal (Nor), suspected incidents (SI), fraud incidents (FI), and fault or power outage (OUT) events. In experimental tests, this study simulates the electricity consumption data involving Nor and NTL events by using SECM for training and validating the “STFT + 1D-CNN-based classifier" model. Through ten-fold cross-validation, the feasibility of the proposed method is evaluated, and the experimental results demonstrate the promising performances, achieving average Recall (%) of 99.12%, average Precision (%) of 87.50%, and average Accuracy (%) of 90.48%, and average F1 Score of 0.9333 for NTL (SI and FI) events detection, and also show average Recall (%) of 99.35%, average Precision (%) of 86.36%, and average Accuracy (%) of 88.89%, and average F1 Score of 0.9268 for NTL and OUT events detection, respectively. These findings confirm the effectiveness and robustness of the proposed classifier model.