Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naïve bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system (FCMIM-SVM) achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.

The deployment of multiple robots for achieving a common goal helps to improve the performance, efficiency, and/or robustness in a variety of tasks. In particular, the observation of moving targets is an important multirobot application that still exhibits numerous open challenges, including the effective coordination of the robots. This paper reviews control techniques for cooperative mobile robots monitoring multiple targets. The simultaneous movement of robots and targets makes this problem particularly interesting, and our review systematically addresses this cooperative multirobot problem for the first time. We classify and critically discuss the control techniques: cooperative multirobot observation of multiple moving targets, cooperative search, acquisition, and track, cooperative tracking, and multirobot pursuit evasion. We also identify the five major elements that characterize this problem, namely, the coordination method, the environment, the target, the robot and its sensor(s). These elements are used to systematically analyze the control techniques. The majority of the studied work is based on simulation and laboratory studies, which may not accurately reflect real-world operational conditions. Importantly, while our systematic analysis is focused on multitarget observation, our proposed classification is useful also for related multirobot applications.

Accurate classification of brain tumors is vital for detecting brain cancer in the Medical Internet of Things. Detecting brain cancer at its early stages is a tremendous medical problem, and many researchers have proposed various diagnostic systems; however, these systems still do not effectively detect brain cancer. To address this issue, we proposed an automatic diagnosing framework that will assist medical experts in diagnosing brain cancer and ensuring proper treatment. In developing the proposed integrated framework, we first integrated a Convolutional Neural Networks model to extract deep features from Magnetic resonance imaging. The extracted features are forwarded to a Long Short Term Memory model, which performs the final classification. Augmentation techniques were applied to increase the data size, thereby boosting the performance of our model. We used the hold-out Cross-validation technique for training and validating our method. In addition, we used various metrics to evaluate the proposed model. The results obtained from the experiments show that our model achieved higher performance than previous models. The proposed model is strongly recommended to be used to diagnose brain cancer in Medical Internet of Things healthcare systems due to its higher predictive outcomes.

Bruxism is a sleep disorder in which the patient clinches and gnashes their teeth. Bruxism detection using traditional methods is time-consuming, cumbersome, and expensive. Therefore, an automatic tool to detect this disorder will alleviate the doctor workload and give valuable help to patients. In this paper, we targeted this goal and designed an automatic method to detect bruxism from the physiological signals using a novel hybrid classifier. We began with data collection. Then, we performed the analysis of the physiological signals and the estimation of the power spectral density. After that, we designed the novel hybrid classifier to enable the detection of bruxism based on these data. The classification of the subjects into “healthy” or “bruxism” from the electroencephalogram channel (C4-A1) obtained a maximum specificity of 92% and an accuracy of 94%. Besides, the classification of the sleep stages such as the wake (w) stage and rapid eye movement (REM) stage from the electrocardiogram channel (ECG1-ECG2) obtained a maximum specificity of 86% and an accuracy of 95%. The combined bruxism classification and the sleep stages classification from the electroencephalogram channel (C4-P4) obtained a maximum specificity of 90% and an accuracy of 97%. The results show that more accurate bruxism detection is achieved by exploiting the electroencephalogram signal (C4-P4). The present work can be applied for home monitoring systems for bruxism detection.

The use of human biometrics is becoming widespread and their major application is human recognition for controlling unauthorized access to both digital services and physical localities. However, the practical deployment of human biometrics for recognition poses a number of challenges, such as template storage capacity, computational requirements, and privacy of biometric information. These challenges are important considerations, in addition to performance accuracy, especially for authentication systems with limited resources. In this paper, we propose wave atom transform (WAT) based palm-vein recognition scheme. The scheme computes, maintains, and matches palm-vein templates with less computational complexity and less storage requirements under secure and privacy-preserving environment. First, we extract palm-vein traits in the WAT domain, which offers sparser expansion and better capability to extract texture features. Then, randomization and quantization are applied to the extracted features to generate a compact, privacy-preserving palm-vein template. We analyze the proposed scheme for its performance and privacy-preservation. The proposed scheme obtains equal error rates (EER) of 1.98%, 0%, 3.05%, and 1.49 for PolyU, PUT, VERA and our palm-vein datasets, respectively. The extensive experimental results demonstrate comparable matching accuracy of the proposed scheme with a minimum template size and computational time of 280 bytes and 0.43 seconds, respectively.

A significant aspect of today’s digital information is attributed networks, which combine multiple node attributes with the basic network topology to extract knowledge. Anomaly Detection on attributed networks has recently drawn significant attention from researchers and is widely used in several high-impact areas. Most current approaches focus on shallow learning methods such as community analysis, ego network or selection of subspace method. These approaches have network sparsity and data nonlinearity problems, and they do not even capture the intricate relationships between various information sources. Deep learning approaches like graph autoencoders are utilized to perform anomaly detection through obtaining node embeddings while dealing with the network nonlinearity and sparsity issues. However, they suffer from the problem of ignoring the latent codes’ embedding distribution, which results in poor representation in many instances. In this paper, we propose a new framework called DVAEGMM to detect anomalies on attributed networks. First, our framework utilizes a dual variational autoencoder for capturing the complex cross-modality relationships between node attributes and network structure, like vanilla autoencoders, but it also considers the potential data distribution and makes use of a generative adversarial network (GAN) for an adversarial regularization approach. An adversarial mechanism makes the encoder make more accurate estimates of how potential features might be distributed. As a result, decoders can make graphs that are more like the original graph. Each input data point is represented by a low-dimensional representation and a probability of reconstruction by the algorithm. Lastly, the Gaussian Mixture Model, a distinct estimation network, is used to approximate the latent vector density, resulting in the detection of anomalies from measuring sample energy. They are trained jointly as an end-to-end framework. DVAEGMM helps in the simultaneous optimization of the mixture model, generative adversarial network, and variational autoencoder parameters. The joint optimization balances the reconstruction probability, the latent representation density approximation, and regularization. Extensive experiments on attributed networks prove that DVAEGMM significantly beats the existing methods, proving the efficiency of the presented approach. The AUC scores of our proposed framework for the BlogCatalog, Flickr, Enron, and Amazon datasets are 0.89380, 0.87130, 0.72480, and 0.75102, respectively.