Wireless sensor network (WSN) comprises numerous compact-sized sensor nodes which are linked to one another. Lifetime maximization of WSN is considered a challenging problem in the design of WSN since its energy-limited capacity of the inbuilt batteries exists in the sensor nodes. Earlier works have focused on the design of clustering and routing techniques to accomplish energy efficiency and thereby result in an increased lifetime of the network. The multihop route selection process can be treated as an NP-hard problem and can be solved by the use of computational intelligence techniques such as fuzzy logic and swarm intelligence (SI) algorithms. With this motivation, this article aims to focus on the design of swarm intelligence with an adaptive neuro-fuzzy inference system-based routing (SI-ANFISR) protocol for clustered WSN. The proposed SI-ANFISR technique aims to determine the cluster heads (CHs) and optimal routes for multihop communication in the network. To accomplish this, the SI-ANFISR technique primarily employs a weighted clustering algorithm to elect CHs and construct clusters. Besides, the SI-ANFISR technique involves the design of an ANFIS model for the selection process, which make use of three input parameters, namely, residual energy, node degree, and node history. In order to optimally adjust the membership function (MF) of the ANFIS model, the squirrel search algorithm (SSA) is utilized. None of the earlier works have used ANFIS with SSA for the routing process. The design of SSA to tune the MFs of the ANFIS model for optimal routing process in WSN shows the novelty of the study. The experimental validation of the SI-ANFISR technique takes place, and the results are inspected under different aspects. The simulation results highlighted the significant performance of the SI-ANFISR technique compared to the recent techniques with a maximum throughput of 43838 kbps and residual energy of 0.4800J, respectively.

Tongue diagnosis is a novel and non-invasive approach commonly employed to carry out the supplementary diagnosis over the globe. Recently, several deep learning (DL) based tongue color image analysis models have existed in the literature for the effective detection of diseases. This paper presents a fusion of handcrafted with deep features based tongue color image analysis (FHDF-TCIA) technique to biomedical applications. The proposed FDHF-TCIA technique aims to investigate the tongue images using fusion model, and thereby determines the existence of disease. Primarily, the FHDF-TCIA technique comprises Gaussian filtering based preprocessing to eradicate the noise. The proposed FHDF-TCIA model encompasses a fusion of handcrafted local binary patterns (LBP) with MobileNet based deep features for the generation of optimal feature vectors. In addition, the political optimizer based quantum neural network (PO-QNN) based classification technique has been utilized for determining the proper class labels for it. A detailed simulation outcomes analysis of the FHDF-TCIA technique reported the higher accuracy of 0.992.

This study investigates the relationships among online learning self-efficacy (OLSE), artificial intelligence anxiety (AIA), and digital well-being (DWB) among undergraduate engineering students from six Jordanian universities (N = 428). Using validated self-report measures and a cross-sectional design, descriptive results indicated moderate OLSE (M = 3.24), moderately high AIA (M = 3.97), and moderate-to-low DWB (M = 2.41). A hypothesized mediation model was supported: OLSE was negatively associated with AIA (β = −0.64, p < .001) and positively associated with DWB (β = 0.52, p < .001), while AIA was negatively associated with DWB (β = −0.33, p < .001). The indirect effect of OLSE on DWB through reduced AIA was significant (β = 0.21, 95% CI [0.12, 0.34]), indicating partial mediation. The final model explained 76% of the variance in DWB. These findings highlight the importance of efficacy-building and AI-literacy supports to reduce AI-related anxiety and promote sustainable DWB in AI-enabled engineering education.