Securing Internet-of-Medical-Things networks using cancellable ECG recognition.

Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks. Among the possible security measures, biometric authentication is becoming a more feasible choice, with a focus on leveraging regularly-monitored biomedical signals like Electrocardiogram (ECG) signals due to their unique characteristics. A notable challenge within all biometric authentication systems is the risk of losing original biometric traits, if hackers successfully compromise the biometric template storage space. Current research endorses replacement of the original biometrics used in access control with cancellable templates. These are produced using encryption or non-invertible transformation, which improves security by enabling the biometric templates to be changed in case an unwanted access is detected. This study presents a comprehensive framework for ECG-based recognition with cancellable templates. This framework may be used for accessing IoMT networks. An innovative methodology is introduced through non-invertible modification of ECG signals using blind signal separation and lightweight encryption. The basic idea here depends on the assumption that if the ECG signal and an auxiliary audio signal for the same person are subjected to a separation algorithm, the algorithm will yield two uncorrelated components through the minimization of a correlation cost function. Hence, the obtained outputs from the separation algorithm will be distorted versions of the ECG as well as the audio signals. The distorted versions of the ECG signals can be treated with a lightweight encryption stage and used as cancellable templates. Security enhancement is achieved through the utilization of the lightweight encryption stage based on a user-specific pattern and XOR operation, thereby reducing the processing burden associated with conventional encryption methods. The proposed framework efficacy is demonstrated through its application on the ECG-ID and MIT-BIH datasets, yielding promising results. The experimental evaluation reveals an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset, alongside an exceptionally large Area under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets. These results underscore the framework potential in securing IoMT networks through cancellable biometrics, offering a hybrid security model that combines the strengths of non-invertible transformations and lightweight encryption.

Optimizing bandwidth utilization and traffic control in ISP networks for enhanced smart agriculture.

As the demand for high-bandwidth Internet connections continues to surge, industries are exploring innovative ways to harness this connectivity, and smart agriculture stands at the forefront of this evolution. In this paper, we delve into the challenges faced by Internet Service Providers (ISPs) in efficiently managing bandwidth and traffic within their networks. We propose a synergy between two pivotal technologies, Multi-Protocol Label Switching-Traffic Engineering (MPLS-TE) and Diffserv Quality of Service (Diffserv-QoS), which have implications beyond traditional networks and resonate strongly with the realm of smart agriculture. The increasing adoption of technology in agriculture relies heavily on real-time data, remote monitoring, and automated processes. This dynamic nature requires robust and reliable high-bandwidth connections to facilitate data flow between sensors, devices, and central management systems. By optimizing bandwidth utilization through MPLS-TE and implementing traffic control mechanisms with Diffserv-QoS, ISPs can create a resilient network foundation for smart agriculture applications. The integration of MPLS-TE and Diffserv-QoS has resulted in significant enhancements in throughput and a considerable reduction in Jitter. Employment of the IPv4 header has demonstrated impressive outcomes, achieving a throughput of 5.83 Mbps and reducing Jitter to 3 msec.

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs.

This paper introduces a method aiming at enhancing the efficacy of speaker identification systems within challenging acoustic environments characterized by noise and reverberation. The methodology encompasses the utilization of diverse feature extraction techniques, including Mel-Frequency Cepstral Coefficients (MFCCs) and discrete transforms, such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and Discrete Wavelet Transform (DWT). Additionally, an Artificial Neural Network (ANN) serves as the classifier for this method. Reverberation is modeled using varying-length comb filters, and its impact on pitch frequency estimation is explored via the Auto Correlation Function (ACF). This paper also contributes to the field of cancelable speaker identification in both open and reverberation environments. The proposed method depends on comb filtering at the feature level, deliberately distorting MFCCs. This distortion, incorporated within a cancelable framework, serves to obscure speaker identities, rendering the system resilient to potential intruders. Three systems are presented in this work; a reverberation-affected speaker identification system, a system depending on cancelable features through comb filtering, and a novel cancelable speaker identification system within reverbration environments. The findings revealed that, in both scenarios with and without reverberation effects, the DWT-based features exhibited superior performance within the speaker identification system. Conversely, within the cancelable speaker identification system, the DCT-based features represent the top-performing choice.

Deep learning innovations in diagnosing diabetic retinopathy: The potential of transfer learning and the DiaCNN model.

Diabetic retinopathy (DR) is a significant cause of vision impairment, emphasizing the critical need for early detection and timely intervention to avert visual deterioration. Diagnosing DR is inherently complex, as it necessitates the meticulous examination of intricate retinal images by experienced specialists. This makes the early diagnosis of DR essential for effective treatment and prevention of eventual blindness. Traditional diagnostic methods, relying on human interpretation of medical images, face challenges in terms of accuracy and efficiency. In the present research, we introduce a novel method that offers superior precision in DR diagnosis, compared to traditional methods, by employing advanced deep learning techniques. Central to this approach is the concept of transfer learning. This entails the utilization of pre-existing, well-established models, specifically InceptionResNetv2 and Inceptionv3, to extract features and fine-tune selected layers to cater to the unique requirements of this specific diagnostic task. Concurrently, we also present a newly devised model, DiaCNN, which is tailored for the classification of eye diseases. To prove the efficacy of the proposed methodology, we leveraged the Ocular Disease Intelligent Recognition (ODIR) dataset, which comprises eight different eye disease categories. The results are promising. The InceptionResNetv2 model, incorporating transfer learning, registered an impressive 97.5% accuracy in both the training and testing phases. Its counterpart, the Inceptionv3 model, achieved an even more commendable 99.7% accuracy during training, and 97.5% during testing. Remarkably, the DiaCNN model showcased unparalleled precision, achieving 100% accuracy in training and 98.3% in testing. These figures represent a significant leap in classification accuracy when juxtaposed with existing state-of-the-art diagnostic methods. Such advancements hold immense promise for the future, emphasizing the potential of our proposed technique to revolutionize the accuracy of DR and other eye disease diagnoses. By facilitating earlier detection and more timely interventions, this approach stands poised to significantly reduce the incidence of blindness associated with DR, thus heralding a new era of improved patient outcomes. Therefore, this work, through its novel approach and stellar results, not only pushes the boundaries of DR diagnostic accuracy but also promises a transformative impact in early detection and intervention, aiming to substantially diminish DR-induced blindness and champion enhanced patient care.

Automated diagnosis of EEG abnormalities with different classification techniques.

Automatic seizure detection and prediction using clinical Electroencephalograms (EEGs) are challenging tasks due to factors such as low Signal-to-Noise Ratios (SNRs), high variance in epileptic seizures among patients, and limited clinical data constraints. To overcome these challenges, this paper presents two approaches for EEG signal classification. One of these approaches depends on Machine Learning (ML) tools. The used features are different types of entropy, higher-order statistics, and sub-band energies in the Hilbert Marginal Spectrum (HMS) domain. The classification is performed using Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbor (KNN) classifiers. Both seizure detection and prediction scenarios are considered. The second approach depends on spectrograms of EEG signal segments and a Convolutional Neural Network (CNN)-based residual learning model. We use 10000 spectrogram images for each class. In this approach, it is possible to perform both seizure detection and prediction in addition to a 3-state classification scenario. Both approaches are evaluated on the Children's Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) dataset, which contains 24 EEG recordings for 6 males and 18 females. The results obtained for the HMS-based model showed an accuracy of 100%. The CNN-based model achieved accuracies of 97.66%, 95.59%, and 94.51% for Seizure (S) versus Pre-Seizure (PS), Non-Seizure (NS) versus S, and NS versus S versus PS classes, respectively. These results demonstrate that the proposed approaches can be effectively used for seizure detection and prediction. They outperform the state-of-the-art techniques for automatic seizure detection and prediction. Block diagram of proposed epileptic seizure detection method using HMS with different classifiers.

Simultaneous Super-Resolution and Classification of Lung Disease Scans.

Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource-limited settings. Chest X-ray and computed tomography (CT) have the potential to serve as effective screening tools for lower respiratory infections, but the use of artificial intelligence (AI) in these areas is limited. To address this gap, we present a computer-aided diagnostic system for chest X-ray and CT images of several common pulmonary diseases, including COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, lung opacity, and various types of carcinoma. The proposed system depends on super-resolution (SR) techniques to enhance image details. Deep learning (DL) techniques are used for both SR reconstruction and classification, with the InceptionResNetv2 model used as a feature extractor in conjunction with a multi-class support vector machine (MCSVM) classifier. In this paper, we compare the proposed model performance to those of other classification models, such as Resnet101 and Inceptionv3, and evaluate the effectiveness of using both softmax and MCSVM classifiers. The proposed system was tested on three publicly available datasets of CT and X-ray images and it achieved a classification accuracy of 98.028% using a combination of SR and InceptionResNetv2. Overall, our system has the potential to serve as a valuable screening tool for lower respiratory disorders and assist clinicians in interpreting chest X-ray and CT images. In resource-limited settings, it can also provide a valuable diagnostic support.

Secure biometric systems based on bio-signals and DNA encryption of optical spectrograms.

Recently, biometrics has become widely used in applications to verify an individual's identity. To address security issues, biometrics presents an intriguing window of opportunity to enhance the usability and security of the Internet of Things (IoT) and other systems. It can be used to secure a variety of newly emerging IoT devices. However, biometric scenarios need more protection against different hacking attempts. Various solutions are introduced to secure biometrics. Cryptosystems, cancelable biometrics, and hybrid systems are efficient solutions for template protection. The new trend in biometric authentication systems is to use bio-signals. In this paper, two proposed authentication systems are introduced based on bio-signals. One of them is unimodal, while the other is multimodal. Protected templates are obtained depending on encryption. The deoxyribonucleic acid (DNA) encryption is implemented on the obtained optical spectrograms of bio-signals. The authentication process relies on the DNA sensitivity to variations in the initial values. In the multimodal system, the singular value decomposition (SVD) algorithm is implemented to merge bio-signals. Different evaluation metrics are used to assess the performance of the proposed systems. Simulation results prove the high accuracy and efficiency of the proposed systems as the equal error rate (EER) value is close to 0 and the area under the receiver operator characteristic curve (AROC) is close to 1. The false accept rate (FAR), false reject rate (FRR), and decidability (D) are also estimated with acceptable results of 1.6 × 10-8, 9.05 × 10-6, and 29.34, respectively. Simulation results indicate the performance stability of the proposed systems in the presence of different levels of noise.

Efficient framework for brain tumor detection using different deep learning techniques.

The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals in medical imaging for the medical diagnosis of several symptoms. In this paper, transfer-learning-based models in addition to a Convolutional Neural Network (CNN) called BRAIN-TUMOR-net trained from scratch are introduced to classify brain magnetic resonance images into tumor or normal cases. A comparison between the pre-trained InceptionResNetv2, Inceptionv3, and ResNet50 models and the proposed BRAIN-TUMOR-net is introduced. The performance of the proposed model is tested on three publicly available Magnetic Resonance Imaging (MRI) datasets. The simulation results show that the BRAIN-TUMOR-net achieves the highest accuracy compared to other models. It achieves 100%, 97%, and 84.78% accuracy levels for three different MRI datasets. In addition, the k-fold cross-validation technique is used to allow robust classification. Moreover, three different unsupervised clustering techniques are utilized for segmentation.

Cancelable biometric system for IoT applications based on optical double random phase encoding.

The security issue is essential in the Internet-of-Things (IoT) environment. Biometrics play an important role in securing the emerging IoT devices, especially IoT robots. Biometric identification is an interesting candidate to improve IoT usability and security. To access and control sensitive environments like IoT, passwords are not recommended for high security levels. Biometrics can be used instead, but more protection is needed to store original biometrics away from invaders. This paper presents a cancelable multimodal biometric recognition system based on encryption algorithms and watermarking. Both voice-print and facial images are used as individual biometrics. Double Random Phase Encoding (DRPE) and chaotic Baker map are utilized as encryption algorithms. Verification is performed by estimating the correlation between registered and tested models in their cancelable format. Simulation results give Equal Error Rate (EER) values close to zero and Area under the Receiver Operator Characteristic Curve (AROC) equal to one, which indicates the high performance of the proposed system in addition to the difficulty to invert cancelable templates. Moreover, reusability and diversity of biometric templates is guaranteed.

Multimodal Medical Image Fusion Using Stacked Auto-encoder in NSCT Domain.

Medical image fusion is a process that aims to merge the important information from images with different modalities of the same organ of the human body to create a more informative fused image. In recent years, deep learning (DL) methods have achieved significant breakthroughs in the field of image fusion because of their great efficiency. The DL methods in image fusion have become an active topic due to their high feature extraction and data representation ability. In this work, stacked sparse auto-encoder (SSAE), a general category of deep neural networks, is exploited in medical image fusion. The SSAE is an efficient technique for unsupervised feature extraction. It has high capability of complex data representation. The proposed fusion method is carried as follows. Firstly, the source images are decomposed into low- and high-frequency coefficient sub-bands with the non-subsampled contourlet transform (NSCT). The NSCT is a flexible multi-scale decomposition technique, and  it is superior to traditional decomposition techniques in several aspects. After that, the SSAE is implemented for feature extraction to obtain a sparse and deep representation from high-frequency coefficients. Then, the spatial frequencies are computed for the obtained features to be used for high-frequency coefficient fusion. After that, a maximum-based fusion rule is applied to fuse the low-frequency sub-band coefficients. The final integrated image is acquired by applying the inverse NSCT. The proposed method has been applied and assessed on various groups of medical image modalities. Experimental results prove that the proposed method could effectively merge the multimodal medical images, while preserving the detail information, perfectly.

Hybrid classification structures for automatic COVID-19 detection.

This paper explores the issue of COVID-19 detection from X-ray images. X-ray images, in general, suffer from low quality and low resolution. That is why the detection of different diseases from X-ray images requires sophisticated algorithms. First of all, machine learning (ML) is adopted on the features extracted manually from the X-ray images. Twelve classifiers are compared for this task. Simulation results reveal the superiority of Gaussian process (GP) and random forest (RF) classifiers. To extend the feasibility of this study, we have modified the feature extraction strategy to give deep features. Four pre-trained models, namely ResNet50, ResNet101, Inception-v3 and InceptionResnet-v2 are adopted in this study. Simulation results prove that InceptionResnet-v2 and ResNet101 with GP classifier achieve the best performance. Moreover, transfer learning (TL) is also introduced in this paper to enhance the COVID-19 detection process. The selected classification hierarchy is also compared with a convolutional neural network (CNN) model built from scratch to prove its quality of classification. Simulation results prove that deep features and TL methods provide the best performance that reached 100% for accuracy.

Efficient multimodal deep-learning-based COVID-19 diagnostic system for noisy and corrupted images.

INTRODUCTION: In humanity's ongoing fight against its common enemy of COVID-19, researchers have been relentless in finding efficient technologies to support mitigation, diagnosis, management, contact tracing, and ultimately vaccination. OBJECTIVES: Engineers and computer scientists have deployed the potent properties of deep learning models (DLMs) in COVID-19 detection and diagnosis. However, publicly available datasets are often adulterated during collation, transmission, or storage. Meanwhile, inadequate, and corrupted data are known to impact the learnability and efficiency of DLMs. METHODS: This study focuses on enhancing previous efforts via two multimodal diagnostic systems to extract required features for COVID-19 detection using adulterated chest X-ray images. Our proposed DLM consists of a hierarchy of convolutional and pooling layers that are combined to support efficient COVID-19 detection using chest X-ray images. Additionally, a batch normalization layer is used to curtail overfitting that usually arises from the convolution and pooling (CP) layers. RESULTS: In addition to matching the performance of standard techniques reported in the literature, our proposed diagnostic systems attain an average accuracy of 98% in the detection of normal, COVID-19, and viral pneumonia cases using corrupted and noisy images. CONCLUSIONS: Such robustness is crucial for real-world applications where data is usually unavailable, corrupted, or adulterated.

Efficient utilization of Hough transform and orthogonal-triangular decomposition for optical wireless modulation format recognition.

Two schemes for optical wireless modulation format recognition (MFR), based on the orthogonal-triangular decomposition (OTD) and Hough transform (HT) of the constellation diagrams, are proposed in this paper. Constellation diagrams are obtained at optical signal-to-noise ratios (OSNRs) ranging from 5 to 30 dB for seven different modulation formats (2/4/8/16-PSK and 8/16/32-QAM) as images. The first scheme depends on applying the HT of the obtained images; the second scheme is based on utilization of the decomposition of each of the obtained image matrices into an orthogonal matrix (Q) and an upper triangular matrix (R) followed by the HT. Different classifiers, including AlexNet, VGG16, and VGG19, are used for the MFR task. Model setups and results are provided to study the scheme efficiency at different levels of OSNR. The proposed schemes provide unique signatures for constellation diagrams. Moreover, it reveals that the main pattern corresponding to each constellation diagram is more distinguishable for both proposed schemes at different levels of OSNR. The obtained results achieve high accuracy at low OSNR values.

Fan-beam projection for modulation classification in optical wireless communication systems.

The focus of most research nowadays in the field of communication technology is on increasing bandwidth in wireless connectivity. Adaptive modulation can be used to enhance efficiency of communication systems. Adaptive modulation requires modulation format identification (MFI) at the receiver side to avoid the overhead required to determine the modulation type at the receiver. We present an MFI algorithm based on fan-beam projection to generate patterns from the constellation diagrams that are more discriminative. The constellation diagrams are obtained as images for eight different modulation formats (2/4/8/16 - PSK and 8/16/32/64 - QAM). Different classifiers such as AlexNet, VGG16, and VGG19 are studied and compared for the task of MFI. Evaluation of this proposed algorithm is performed by estimating the classification accuracy at different optical signal-to-noise ratios (OSNRs) ranging from 5 to 30 dB. The simulation results reveal that the proposed algorithm succeeds in identifying the wireless optical modulation format blindly with a classification accuracy up to 100% even at low OSNR values less than 8 dB compared with the related work.

Deploying Machine Learning Techniques for Human Emotion Detection.

Emotion recognition is one of the trending research fields. It is involved in several applications. Its most interesting applications include robotic vision and interactive robotic communication. Human emotions can be detected using both speech and visual modalities. Facial expressions can be considered as ideal means for detecting the persons' emotions. This paper presents a real-time approach for implementing emotion detection and deploying it in the robotic vision applications. The proposed approach consists of four phases: preprocessing, key point generation, key point selection and angular encoding, and classification. The main idea is to generate key points using MediaPipe face mesh algorithm, which is based on real-time deep learning. In addition, the generated key points are encoded using a sequence of carefully designed mesh generator and angular encoding modules. Furthermore, feature decomposition is performed using Principal Component Analysis (PCA). This phase is deployed to enhance the accuracy of emotion detection. Finally, the decomposed features are enrolled into a Machine Learning (ML) technique that depends on a Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), or Random Forest (RF) classifier. Moreover, we deploy a Multilayer Perceptron (MLP) as an efficient deep neural network technique. The presented techniques are evaluated on different datasets with different evaluation metrics. The simulation results reveal that they achieve a superior performance with a human emotion detection accuracy of 97%, which ensures superiority among the efforts in this field.

Deep-learning-based seizure detection and prediction from electroencephalography signals.

Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or neuro-physiologists; a process that is considered to have a comparatively low inter-rater agreement. Furthermore, the new data interpretation consumes an excessive amount of time and resources. Hence, an automatic seizure detection and prediction system can improve the quality of patient care in terms of shortening the diagnosis period, reducing manual errors, and automatically detecting debilitating events. Moreover, for patient treatment, it is important to alert the patients of epilepsy seizures prior to seizure occurrence. Various distinguished studies presented good solutions for two-class seizure detection problems with binary classification scenarios. To deal with these challenges, this paper puts forward effective approaches for EEG signal classification for normal, pre-ictal, and ictal activities. Three models are presented for the classification task. Two of them are patient-specific, while the third one is patient non-specific, which makes it better for the general classification tasks. The two-class classification is implemented between normal and pre-ictal activities for seizure prediction and between normal and ictal activities for seizure detection. A more generalized three-class classification framework is considered to identify all EEG signal activities. The first model depends on a Convolutional Neural Network (CNN) with residual blocks. It contains thirteen layers with four residual learning blocks. It works on spectrograms of EEG signal segments. The second model depends on a CNN with three layers. It also works on spectrograms. On the other hand, the third model depends on Phase Space Reconstruction (PSR) to eliminate the limitations of the spectrograms used in the first models. A five-layer CNN is used with this strategy. The advantage of the PSR is the direct projection from the time domain, which keeps the main trend of different signal activities. The third model deals with all signal activities, and it was tested for all patients of the CHB-MIT dataset. It has a superior performance compared to the first models and the state-of-the-art models.

Efficient deep learning approach for augmented detection of Coronavirus disease.

The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date. Due to the limited availability of annotated Coronavirus X-ray and CT images, the detection of COVID-19 remains the biggest challenge in diagnosing this disease. This paper provides a promising solution by proposing a COVID-19 detection system based on deep learning. The proposed deep learning modalities are based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM). Two different datasets are adopted for the simulation of the proposed modalities. The first dataset includes a set of CT images, while the second dataset includes a set of X-ray images. Both of these datasets consist of two categories: COVID-19 and normal. In addition, COVID-19 and pneumonia image categories are classified in order to validate the proposed modalities. The proposed deep learning modalities are tested on both X-ray and CT images as well as a combined dataset that includes both types of images. They achieved an accuracy of 100% and an F1 score of 100% in some cases. The simulation results reveal that the proposed deep learning modalities can be considered and adopted for quick COVID-19 screening.

An efficient multi-biometric cancellable biometric scheme based on deep fusion and deep dream.

Today, biometrics are the preferred technologies for person identification, authentication, and verification cutting across different applications and industries. Sadly, this ubiquity has invigorated criminal efforts aimed at violating the integrity of these modalities. Our study presents a multi-biometric cancellable scheme (MBCS) that exploits the proven utility of deep learning models to fuse multi-exposure fingerprint, finger vein, and iris biometrics by using an Inspection V3 pre-trained model to generate an aggregate tamper-proof cancellable template. To validate our MBCS, we employed an extensive evaluation including visual, quantitative, and qualitative assessments as well as complexity analysis where average outcomes of 99.158%, 24.523 dB, 0.079, 0.909, 59.582 and 23.627 were recorded for NPCR, PSNR, SSIM, UIQ, SD and UACI respectively. These quantitative outcomes indicate that the proposed scheme compares favourably against state-of-the-art methods reported in the literature. To further improve the utility of the proposed MBCS, we are exploring its refinement to facilitate generation of cancellable templates for real-time biometric applications in person authentication at airports, banks, etc.

Efficient anomaly detection from medical signals and images with convolutional neural networks for Internet of medical things (IoMT) systems.

Deep learning is one of the most promising machine learning techniques that revolutionalized the artificial intelligence field. The known traditional and convolutional neural networks (CNNs) have been utilized in medical pattern recognition applications that depend on deep learning concepts. This is attributed to the importance of anomaly detection (AD) in automatic diagnosis systems. In this paper, the AD is performed on medical electroencephalography (EEG) signal spectrograms and medical corneal images for Internet of medical things (IoMT) systems. Deep learning based on the CNN models is employed for this task with training and testing phases. Each input image passes through a series of convolution layers with different kernel filters. For the classification task, pooling and fully-connected layers are utilized. Computer simulation experiments reveal the success and superiority of the proposed models for automated medical diagnosis in IoMT systems.

Secure Health Monitoring Communication Systems Based on IoT and Cloud Computing for Medical Emergency Applications.

Smart health surveillance technology has attracted wide attention between patients and professionals or specialists to provide early detection of critical abnormal situations without the need to be in direct contact with the patient. This paper presents a secure smart monitoring portable multivital signal system based on Internet-of-Things (IoT) technology. The implemented system is designed to measure the key health parameters: heart rate (HR), blood oxygen saturation (SpO2), and body temperature, simultaneously. The captured physiological signals are processed and encrypted using the Advanced Encryption Standard (AES) algorithm before sending them to the cloud. An ESP8266 integrated unit is used for processing, encryption, and providing connectivity to the cloud over Wi-Fi. On the other side, trusted medical organization servers receive and decrypt the measurements and display the values on the monitoring dashboard for the authorized specialists. The proposed system measurements are compared with a number of commercial medical devices. Results demonstrate that the measurements of the proposed system are within the 95% confidence interval. Moreover, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Relative Error (MRE) for the proposed system are calculated as 1.44, 1.12, and 0.012, respectively, for HR, 1.13, 0.92, and 0.009, respectively, for SpO2, and 0.13, 0.11, and 0.003, respectively, for body temperature. These results demonstrate the high accuracy and reliability of the proposed system.