Energy efficiency is considered the major design issue in wireless sensor networks (WSN), which can be addressed using clustering and routing techniques. They are treated as Non-deterministic Polynomial (NP)-hard optimization problems and are solved using metaheuristic algorithms to identify the optimal or near-optimal solutions. With this motivation, this paper develops a hybridization of the metaheuristic cluster-based routing (HMBCR) technique for WSN. The HMBCR technique initially involves a brainstorm optimization with levy distribution (BSO-LD) based clustering process using a fitness function incorporating four parameters such as energy, distance to neighbors, distance to the base station, and network load. Besides, a water wave optimization with a hill-climbing (WWO-HC) based routing process is carried out for optimal route selection. Extensive experimentation analysis is performed to ensure the energy efficiency and network lifetime performance of the HMBCR technique. The experimental outcome ensured the superior results of the HMBCR technique over the compared methods under different aspects.

Brain tumour (BT) detection involves the process of identifying the presence of a brain tumour in medical imaging, such as MRI scans. BT detection often relies on medical imaging techniques, such as MRI (Magnetic Resonance Imaging), CT (Computed Tomography), or PET (Positron Emission Tomography) scans. Early detection of BT is important and MRI is one of the primary imaging techniques used to diagnose and assess BT. Deep learning (DL) techniques, particularly convolutional neural networks (CNNs) have shown promising results in assisting with BT detection on MRI scans. This study designs an Ensemble Learning Driven Computer-Aided Diagnosis Model for Brain Tumor Classification (ELCAD-BTC) technique on MRIs. The presented system purposes to detect and classify various steps of BTs. The presented system contains a Gabor filtering (GF) approach to remove the noise and increase the quality of MRI images. Moreover, ensemble learning of three DL models namely EfficientNet, DenseNet, and MobileNet is utilized as feature extractors. Furthermore, the denoising autoencoder (DAE) approach can be exploited to detect the presence of BTs. Finally, a social spider optimization algorithm (SSOA) was carried out for the hyperparameter tuning of the DL models. For simulating the improved BT classification outcome, a brief set of simulations occur on BRATS 2015 database.

The present trends in smart world reflects the extensive use of limited resources through information and communication technology. The limited resources like space, mobility, energy, etc., have been consumed rigorously towards creating optimized but smart instances. Thus, a new concept of IoT integrated smart city vision is yet to be proposed which includes a combination of systems like noise and air loss monitoring, web monitoring and fire detection systems, smart waste bin systems, etc., that have not been clearly addressed in the previous researches. This paper focuses on developing an effective system for possible monitoring of losses, traffic management, thus innovating smart city at large with digitalized and integrated systems and software for fast and effective implementations. In our proposed system, a real time data analysis is performed. These data are collected by various sensors to analyze different factors that are responsible for such losses. The proposed work is validated on a real case study.

The rise of cyber-physical-social systems (CPSS) as a novel paradigm has revolutionized the relationship among humans, computers and physical environment. The key technologies to design CPSS directly related to multi-disciplinary technologies including cyber-physical systems (CPS) and cyber-social systems (CSS). Unfortunately, the design of CPSS is not an easier process because of the network heterogeneity, complex hardware and software entities. At the same time, fog computing is emerged as an expansion of cloud computing which efficiently addresses the abovementioned issue. Resource provisioning is a main technology involved in fog computing. This paper devises a novel fuzzy clustering with flower pollination algorithm called FCM-FPA as a resource provisioning model for fog computing. At the earlier stage, the resource attributes are standardized and normalized. Next, the fuzzy clustering with FPA is developed for partitioning the resources and the scalability of resource searching has been minimized. At last, the presented resource provisioning algorithm based on optimized fuzzy clustering has been devised. The performance of the proposed FCM-FPA model has been tested using a set of two benchmark Iris and Wine dataset. The experimental outcome ensured that the FCM-FPA model has shown proficient results over the compared methods by offering maximum user satisfaction and effective resource provisioning.

Driver drowsiness detection is a significant element of Advanced Driver-Assistance Systems (ADASs), which utilize deep learning (DL) methods to improve road safety. A driver drowsiness detection system can trigger timely alerts like auditory or visual warnings, thereby stimulating drivers to take corrective measures and ultimately avoiding possible accidents caused by impaired driving. This study presents a Deep Learning-based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems (DLID3-ADAS) technique. The DLID3-ADAS technique aims to enhance road safety via the detection of drowsiness among drivers. Using the DLID3-ADAS technique, complex features from images are derived through the use of the ShuffleNet approach. Moreover, the Northern Goshawk Optimization (NGO) algorithm is exploited for the selection of optimum hyperparameters for the ShuffleNet model. Lastly, an extreme learning machine (ELM) model is used to properly detect and classify the drowsiness states of drivers. The extensive set of experiments conducted based on the Yawdd driver database showed that the DLID3-ADAS technique achieves a higher performance compared to existing models, with a maximum accuracy of 97.05% and minimum computational time of 0.60 s.