Journal of Intelligent Systems and Internet of Things

Excessive fats in human body results in obesity, which is generally linked to various illness like heart diseases, diabetes, etc. Therefore, determining the quantity of body fat becomes essential to save the human health. Though numerous approaches are available in determining body fat percentage (BFP), intelligent and accurate models can be designed using artificial intelligence (AI) techniques. Conventional single stage methods utilized particular readings from the body or explanatory parameters in predicting BFP. In this view, this study develops a new Gravitational Search Optimization with Neutrosophic rule-based Body Fat Percentage Prediction model. The presented model intends to appropriately determine the level of BFP in an effective and automated way. To accomplish this, the proposed model follows a two-stage process namely prediction and parameter optimization. At the initial stage, the model derives a new neutrosophic set based rule classifier to determine the BFP. Secondly, the membership function in the rule based model is optimally chosen by the use of GSO algorithm and thereby results in enhanced predictive outcomes of the classification model. A wide ranging simulation analysis is performed and the results are inspected under several dimensions.

Coronavirus Illness 2019 (COVID-19), a rare disease carried by a coronavirus known as a novel coronavirus, is now posing a danger to the whole planet. Despite the rising number of cases, there is no commercially available vaccination for COVID-19. The moderate symptoms of COVID-19 illness, on the other hand, may be treated with a variety of antiviral treatments. Even yet, selecting the optimum antiviral medication to manage the moderate symptom of COVID-19 is a difficult and ambiguous option. Selecting a drug might be challenging. Fuzzy collaborative intelligence (FCI) was presented in this research as a solution to solve the difficulty of evaluating the appropriateness of a drug selection. In the FCI method, the fuzzy inverse of column sum, partial consensus fuzzy intersection, and fuzzy procedure for order preference by similarity to the ideal solution.  To show the practicality and usefulness of the created approach in real-world applications, a case study of medication choice for COVID-19 illness is being investigated.

Cloud computing (CC) becomes a familiar topic in offering unlimited access to services as well as resources via the Internet. A comprehensive CC management system is needed to collect details of the task processing and ensure proper resource allocation with the accomplishment of Quality of Service (QoS). At the same time, virtual machine (VM) migration is a crucial problem in the CC platform which contributes to energy utilization and resource usage. Therefore, this paper presents a new energy-aware elephant herd optimization-based VM migration (EAEHO-VMM) scheme. The EAEHO-VMM algorithm aims to migrate the VMs and prediction failure VMs. At the initial stage, the EHO algorithm is executed to minimize the energy utilization of the VM migration process in the CC environment. In addition, a support vector machine (SVM) model is applied to identify the failure VMs and allows relocation in an effective way. In order to make sure the better performance of the EAEHO-VMM algorithm, a series of simulations take place, and the results are investigated in terms of different aspects. The experimental outcomes ensured the enhanced VM migration performance of the EAEHO-VMM algorithm over the other techniques.

Cyber security practices mainly involve the prevention of external threats to software, hardware, server data, and other assets which are connected to the internet. Organizations follow a lot of cyber security practices to protect their systems and databases from malicious cyber actors. Cybercriminals use different techniques like spear-phishing, phishing, password attack, denial of service, ransomware, etc. to cause harm to people, organizations, and governments and steal important information from them. We analyzed the use of deep learning algorithms to deal with cyber-attacks. Deep neural networks or deep learning consist of machine learning procedures to support the network to fix complex issues and learn from unmanaged data. In addition, we also analyzed some of the cyber security laws and practices implemented in the US and Saudi Arabia to work collaboratively against cyber threats. It is observed that both countries are doing well against cyberthreats, but they need to work even more to provide training and support to professionals in the public sector who handle sensitive data about cyber security.

Due to the exponential increase in video data, an automated examination of videos has become essential. A significant requirement is the capability of the automated video summarization process, which is helpful in vast application areas from surveillance to security. It assists in monitoring the user application with reduced memory and time. Therefore, this paper designs an automated deep learning-based video summarization approach for forest fire detection (ADLVS-FFD). The ADLVS-FFD technique aims to summarize the captured videos and detects the existence of forest fire in it. In addition, the ADLVS-FFD technique involves different subprocesses such as frame splitting, feature extraction, and classification. Besides, a merged Gaussian mixture model (MGMM) is used to extract keyframes and features. Moreover, the long short-term memory (LSTM) model is employed to detect and classify input images into normal and forest fire images. To ensure the better performance of the ADLVS-FFD technique, a comprehensive experimental validation process takes place on a benchmark video dataset. The resultant experimental validation process highlighted the supremacy of the ADLVS-FFD technique over the recent methods.