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This paper tactics to implement the attack detection in medical Internet of things (IoT) devices using improved deep learning architecture for accomplishing the concept bring your own device (BYOD). Here, a simulation-based hospital environment is modeled where many IoT devices or medical equipment are communicated with each other. The node or the device, which is creating the attack are recognized with the support of attribute collection. The dataset pertaining to the attack detection in medical IoT is gathered from each node that is considered as features. These features are subjected to a deep belief network (DBN), which is a part of deep learning algorithm. Despite the existing DBN, the number of hidden neurons of DBN is tuned or optimized correctly with the help of a hybrid meta-heuristic algorithm by merging grasshopper optimization algorithm (GOA) and spider monkey optimization (SMO) in order to enhance the accuracy of detection. The hybrid algorithm is termed as local leader phase-based GOA (LLP-GOA). The DBN is used to train the nodes by creating the data library with attack details, thus maintaining accurate detection during testing.

This paper has presented novel attack detection in medical IoT devices using improved deep learning architecture as BYOD. With this, this paper aims to show the high convergence and better performance in detecting attacks in the hospital network.

From the analysis, the overall performance analysis of the proposed LLP-GOA-based DBN in terms of accuracy was 0.25% better than particle swarm optimization (PSO)-DBN, 0.15% enhanced than grey wolf algorithm (GWO)-DBN, 0.26% enhanced than SMO-DBN and 0.43% enhanced than GOA-DBN. Similarly, the accuracy of the proposed LLP-GOA-DBN model was 13% better than support vector machine (SVM), 5.4% enhanced than k-nearest neighbor (KNN), 8.7% finer than neural network (NN) and 3.5% enhanced than DBN.

This paper adopts a hybrid algorithm termed as LLP-GOA for the accurate detection of attacks in medical IoT for improving the enhanced security in healthcare sector using the optimized deep learning. This is the first work which utilizes LLP-GOA algorithm for improving the performance of DBN for enhancing the security in the healthcare sector.

A huge variety of devices accumulates as well distributes a large quantity of data either with the help of wired networks or wireless networks to implement a wide variety of application scenarios. The spectrum resources on the other hand become extremely unavailable with the development of communication devices and thereby making it difficult to transmit data on time.

The spectrum resources on the other hand become extremely unavailable with the development of communication devices and thereby making it difficult to transmit data on time. Therefore, the technology of cognitive radio (CR) is considered as one of the efficient solutions for addressing the drawbacks of spectrum distribution whereas the secondary user (SU) performance is significantly influenced by the spatiotemporal instability of spectrum.

As a result, the technique of the hybrid filter detection network model (HFDNM) is suggested in this research work under various SU relationships in the networks of CR. Furthermore, a technique of hybrid filter detection was recommended in this work to enhance the performance of idle spectrum applications. When compared to other existing techniques, the suggested research work achieves enhanced efficiency with respect to both throughputs as well as delay.

The proposed HFDNM improved the transmission delay at 3 SUs with 0.004 s/message and 0.008 s/message when compared with existing NCNC and NNC methods in case of number of SUs and also improved 0.02 s/message and 0.08 s/message when compared with the existing methods of NCNC and NNC in case of channel loss probability at 0.3.

This paper provides, first, a historical perspective of accounting research relating to Asian/Pacific countries as seen from the vantage of the leading international journal in the United States and, second, a bibliographical data base and index of twenty‐six years of articles on this region of the world. It accomplishes the first objective by presenting a tabular profile of research in international accounting as it pertains to countries in the Asian/Pacific Rim region as shown in articles published in the International Journal of Accounting (formerly, the International Journal of Accounting, Education and Research) and related publications which appeared from 1965 to 1990. The articles are classified according to country, research methodology, subject, and five‐year time periods. The paper accomplishes the second objective by providing an annotated bibliography of 125 articles on Asian/Pacific Rim countries and indices by country and methodology, and subject.

This paper aims to investigate the effect of shear rate, concentration (4–20 kg/m³) and temperature (20°C–60 °C) on the apparent viscosity of apricot gum solutions.

Apparent viscosity has been measured using a rotational viscometer.

It has been observed that the shear stress and apparent viscosity values increase at high concentrations in the prepared apricot gum solutions. However, it is understood that the higher the temperature in the operation conditions, the lower the apparent viscosity results. Power-law is found the best-fitting model to illustrate the changes in temperature and concentration. According to the consistency coefficient and flow behavior indices, the apricot gum displayed shear-thinning behavior (pseudoplastic). The apricot gum is a polysaccharide with amino and uronic acids, according to Fouirer Transform Infrared Spektrofotometre spectra.

The results suggest that power-law model can be used to estimate the viscosity of apricot gum solutions at different temperatures and concentrations for applications for which flow behavior should be taken into account.

Exudate gums have good rheological properties and, therefore, are widely used in the food industry. Apricot gum is a biodegradable and abundant polysaccharide that enhances viscosity, stabilizes suspension or emulsion and improves the flow properties of foods. Different rheological models are used to investigate rheological properties. However, those models are time-independent to fit the experimental data.