The leading cause of death among people around the world is cardiovascular disease (CVD). In order to prevent patients from other damages, precise diagnostics of CVD on time is a crucial factor. Researcher workers are inspired to apply machine learning (ML) for accurate and quick diagnosis of CVD. ML algorithm extracts patterns and hidden relationships in the medical dataset for detecting or predicting disease development. But the prediction of CVD is a challenging task. The increasing size of healthcare dataset has made it a complex task for practitioners to make disease predictions and understand the feature relations. And so, selection of crucial features in a dataset plays a key role in optimizing the performance of ML algorithm. This study develops an Automated Cardiovascular Disease Diagnosis using Honey Badger Optimization with Modified Deep Learning (ACVD-HBOMDL) Model. The major aim of the ACVD-HBOMDL technique lies in the classification of CVD using feature selection (FS) and hyperparameter tuning strategies. Initially, the ACVD-HBOMDL technique applies min-max scaler to preprocess the medical data. To elect an optimal subset of features, the HBO algorithm is used in this work. For CVD classification, deep learning modified neural network (DLMNN) classifier is used and its hyperparameters can be optimally chosen by Bayesian optimization. The experimental results of the ACVD-HBOMDL technique can be tested on benchmark medical dataset and the obtained results demonstrate the significant outcomes of the ACVD-HBOMDL technique over other existing techniques.

A key component of wireless sensor networks (WSN) is optimal coverage. For WSN to have network lifetime and optimal resource utilization, maximum coverage must be guaranteed. The unequal distribution of sensor nodes in densely populated regions contributes to the build-up of network coverage. This research suggests a revolutionary intelligent deep learning-based optimization approach for WSN coverage optimization to tackle this problem. Here, the optimal network coverage of WSN is performed by the novel enhanced deep Q-network (EDQN) algorithm, where the parameters of DQN are tuned by nature inspired optimization algorithm called hippopotamus optimization (HO) with the intention of attaining the fitness function. In order to maximize coverage, the node position is changed in the developed methodology to represent the spatial properties of the network. Along with lowering latency, the increased coverage also increases throughput and network longevity. The outcome of several network coverage optimization trials is computed, and the EDQN-HO’s impact on network coverage optimization is also determined by adjusting the parameters. The network coverage optimization studies’ simulated findings demonstrate that the suggested EDQN-HO may be effectively utilized in a variety of settings. The proposed EDQN-HO for the WSN coverage model returns superior outcomes with 18.31%, 78.95%, 10.65%, 87.5%, 83.33%, and 38.20% than the existing methods in terms of coverage rate, energy consumption, computing time, positioning error, network lifetime, and average moving distance respectively.

Cochlear implant (CI) electrode arrays must navigate the delicate, spiraling microanatomy of the human cochlea. Optimizing their intrinsic mechanical properties is crucial for ensuring smooth surgical insertion and preventing extracochlear buckling. This study presents a parametric, bench‐type biomechanical evaluation of tapered cochlear electrode arrays, combining three‐dimensional (3D) finite element analysis (FEA) with a design of experiments (DOE) methodology. The array was modeled as a heterogeneous composite, comprising platinum‐iridium (Pt‐Ir) conductors embedded in a polydimethylsiloxane (PDMS) (silicone) matrix and evaluated as a free‐space cantilever under simulated surgical deflection conditions of up to 30°. This approach isolates the intrinsic bending stiffness and longitudinal column strength independent of complex tribological friction. A 15‐run factorial design varying apical radius, basal radius, and array length was utilized to quantify their interactive effects on tip deflection and reaction force. The FEA results demonstrated that across the parametric sweeps, maximum tip deflection ranged from 6.16 to 8.27 mm, while the reaction force varied between 1.096 and 4.66 mN. Peak Von Mises stress localized at the fixed basal end at 205.86 MPa, operating safely within the elastic limit of the composite’s alloy. Analysis of variance (ANOVA) revealed that array length is the dominant driver of tip deflection, whereas the basal radius governs reaction force due to its fourth‐power scaling of the area moment of inertia. Predictive regression models achieved adjusted R ‐squared values approaching unity; as the experimental runs are derived from deterministic FEA simulations rather than stochastic physical trials, this near‐perfect fit reflects exact mathematical mapping of the response surface rather than real‐world physical variance. To validate this deterministic numerical framework, the outputs were successfully correlated against previously published experimental data using optical fibers as structural proxies under precision force measurement. Ultimately, these findings provide an efficient, predictive parametric design framework for benchmarking and comparing tapered electrode array geometries, utilizing flexural rigidity and reaction forces as fundamental proxies for safe surgical handling and structural trackability.