Data classification effectively classifies the data based on the labeled class distribution. To classify the data using the imbalanced distribution poses a significant challenge in the class inequity problem. Various data classification methods are developed in the learning framework, but proving better classification accuracy is a significant challenge in the application domain. Therefore, an effective classification method named Adam-Cuckoo search based Deep Belief Network (Adam-CS based DBN) is proposed to perform the classification process. At first, the input data is forwarded to the pre-processing stage, and then the feature selection stage. The wrapper-based feature selection model conducts the search in space with the possible parameters. The operators specify the connectivity between the states and select the features based on their state. The classification is performed using the Deep Belief Network (DBN) classifier such that the multilayer perceptron (MLP) layer of Deep Belief Network (DBN) is trained using the proposed Adam based Cuckoo search (Adam-CS) algorithm. The breeding behavior of cuckoos is integrated with the step size parameter to enhance the accuracy of the classification process. The adaptive learning rate parameter effectively estimates the moments using a sparse gradient. The proposed Adam based Cuckoo search (Adam-CS) algorithm attained better performance using the metrics, such as accuracy, specificity, and sensitivity, with 90% training data.
The continuous increase in the use of smart devices and the need for E2E smart2smart (S2S) services in IoT systems play effective and contemporary roles in the field of communication, and a large amount of resources is required. Thus, IoTs and cloud computing must be integrated. One of the results of this integration is the increase in the number of attacks and vulnerabilities in the E2E S2S message delivery service of such an IoT-cloud system. However, none of the traditional security solutions can be sufficiently regarded as a secure and lightweight mechanism for ensuring that the security requirements for E2E S2S message transmission in the IoT-cloud system are fulfilled. This work aims to provide an efficient and secure, lightweight E2E S2S message delivery function, which includes the E2E S2S secure key and biometric parameter exchange function, a bio-shared parameter and bio-key generation function, secure lightweight E2E S2S communication negotiation and secure E2E S2S lightweight message delivery. The secure, lightweight cryptographic communication procedure is negotiated between a pair of smart devices during each E2E session to minimize the power consumption required of limited-energy devices. Such a negotiation process prevents known attacks by providing responsive mutual authentication. Lightweight message delivery by the two smart devices can satisfy the basic security requirements of E2E communication and ensure that the computational cost required for a real-time system is as low as possible. INDEX TERMS Message delivery function, IoT-cloud system, smart devices, E2E S2S, mutual authentication.
Artificial intelligence (AI) is simulating human intelligence processes by machines and software simulators to help humans in making accurate, informed, and fast decisions based on data analysis. The medical field can make use of such AI simulators because medical data records are enormous with many overlapping parameters. Using in-depth classification techniques and data analysis can be the first step in identifying and reducing the risk factors. In this research, we are evaluating a dataset of cardiovascular abnormalities affecting a group of potential patients. We aim to employ the help of AI simulators such as Weka to understand the effect of each parameter on the risk of suffering from cardiovascular disease (CVD). We are utilizing seven classes, such as baseline accuracy, naïve Bayes, <em>k</em>-nearest neighbor, decision tree, support vector machine, linear regression, and artificial neural network multilayer perceptron. The classifiers are assisted by a correlation-based filter to select the most influential attributes that may have an impact on obtaining a higher classification accuracy. Analysis of the results based on sensitivity, specificity, accuracy, and precision results from Weka and Statistical Package for Social Sciences (SPSS) is illustrated. A decision tree method (J48) demonstrated its ability to classify CVD cases with high accuracy 95.76%.
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