The Internet of Drone Things (IoDT) is a trending research area where drones are used to gather information from ground networks. In order to overcome the drawbacks of the Internet of Vehicles (IoV), such as congestion issues, security issues, and energy consumption, drones were introduced into the IoV, which is termed drone-assisted IoV. Due to the unique characteristics of the IoV, such as dynamic mobility and unsystematic traffic patterns, the performance of the network is reduced in terms of delay, energy consumption, and overhead. Additionally, there is the possibility of the existence of various attackers that disturb the traffic pattern. In order to overcome this drawback, the drone-assisted IoV was developed. In this paper, the bio-inspired dynamic trust and congestion-aware zone-based secured Internet of Drone Things (BDTC-SIoDT) is developed, and it is mainly divided into three sections. These sections are dynamic trust estimation, congestion-aware community construction, and hybrid optimization. Initially, through the dynamic trust estimation process, triple-layer trust establishment is performed, which helps to protect the network from all kinds of threats. Secondly, a congestion-aware community is created to predict congestion and to avoid it. Finally, hybrid optimization is performed with the combination of ant colony optimization (ACO) and gray wolf optimization (GWO). Through this hybrid optimization technique, overhead occurs during the initial stage of transmission, and the time taken by vehicles to leave and join the cluster is reduced. The experimentation is performed using various threats, such as flooding attack, insider attack, wormhole attack, and position falsification attack. To analyze the performance, the parameters that are considered are energy efficiency, packet delivery ratio, routing overhead, end-to-end delay, packet loss, and throughput. The outcome of the proposed BDTC-SIoDT is compared with earlier research works, such as LAKA-IOD, NCAS-IOD, and TPDA-IOV. The proposed BDTC-SIoDT achieves high performance when compared with earlier research works.

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Nowadays, smart healthcare and biomedical research have marked a substantial growth rate in terms of their presence in the literature, computational approaches, and discoveries, owing to which a massive quantity of experimental datasets was published and generated (Big Data) for describing and validating such novelties. Drug-drug interaction (DDI) significantly contributed to drug administration and development. It continues as the main obstacle in offering inexpensive and safe healthcare. It normally happens for patients with extensive medication, leading them to take many drugs simultaneously. DDI may cause side effects, either mild or severe health problems. This reduced victims' quality of life and increased hospital healthcare expenses by increasing their recovery time. Several efforts were made to formulate new methods for DDI prediction to overcome this issue. In this aspect, this study designs a new Spotted Hyena Optimizer Driven Deep Learning based Drug-Drug Interaction Prediction (SHODL-DDIP) model in a big data environment. In the presented SHODL-DDIP technique, the relativity and characteristics of the drugs can be identified from different sources for prediction. The input data is preprocessed at the primary level to improve its quality. Next, the salp swarm optimization algorithm (SSO) is used to select features. In this study, the deep belief network (DBN) model is exploited to predict the DDI accurately. The SHO algorithm is involved in improvising the DBN model's predictive outcomes, showing the novelty of the work. The experimental result analysis of the SHODL-DDIP technique is tested using drug databases, and the results signified the improvements of the SHODL-DDIP technique over other recent models in terms of different performance measures.

The major mortality factor relevant to the intestinal tract is the growth of tumorous cells (polyps) in various parts. More specifically, colonic polyps have a high rate and are recognized as a precursor of colon cancer growth. Endoscopy is the conventional technique for detecting colon polyps, and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss rate. The automated diagnosis of polyps in a computer-aided diagnosis (CAD) method is implemented using statistical analysis. Nowadays, Deep Learning, particularly through Convolution Neural networks (CNN), is broadly employed to allow the extraction of representative features. This manuscript devises a new Northern Goshawk Optimization with Transfer Learning Model for Colonic Polyp Detection and Classification (NGOTL-CPDC) model. The NGOTL-CPDC technique aims to investigate endoscopic images for automated colonic polyp detection. To accomplish this, the NGOTL-CPDC technique comprises of adaptive bilateral filtering (ABF) technique as a noise removal process and image pre-processing step. Besides, the NGOTL-CPDC model applies the Faster SqueezeNet model for feature extraction purposes in which the hyperparameter tuning process is performed using the NGO optimizer. Finally, the fuzzy Hopfield neural network (FHNN) method can be employed for colonic poly detection and classification. A widespread simulation analysis is carried out to ensure the improved outcomes of the NGOTL-CPDC model. The comparison study demonstrates the enhancements of the NGOTL-CPDC model on the colonic polyp classification process on medical test images.