ABSTRACT Security in wireless sensor networks (WSNs) is of paramount importance due to their pervasive deployment in critical infrastructure and sensitive environments. Despite their ubiquitous nature, WSNs are vulnerable to various security threats, ranging from unauthorized access to data manipulation and network disruption. In response to these challenges, this paper proposes a novel approach leveraging the Base Stacked Long Short‐Term Memory with Attention Models and AdaBoost Ensemble (BSLAM‐AE) architecture to enhance security in WSNs. The proposed model is designed to address the unique characteristics and challenges of WSNs, combining deep learning and ensemble learning techniques to detect and mitigate security threats effectively. The BSLAM‐AE model incorporates stacked LSTM networks with attention mechanisms, enabling the analysis of time‐series data and the detection of subtle anomalies or security breaches. In addition, an AdaBoost ensemble‐learning component iteratively trains a set of models to improve predictive accuracy and robustness. Implemented in the PyCharm integrated development environment, experimental results demonstrate the efficacy of the proposed model, achieving an impressive accuracy of 98% in detecting security threats in WSNs. Overall, the BSLAM‐AE model represents a significant advancement in WSN security, offering a comprehensive and efficient solution for detecting and mitigating security threats. By leveraging deep learning and ensemble learning techniques, the proposed model provides enhanced security and reliability, thereby safeguarding WSNs against potential attacks and ensuring the integrity and availability of critical data and infrastructure.

Summary Manual investigation of blood cell count is sometimes erroneous due to interoperability error, fatigue error, requiring expert skill and time consuming too. In particular, investigation of white blood cell (WBC) gains importance in identifying diseases like leukemia, leukopenia, etc. WBC does not possess regular structure because they move throughout the blood stream and hence analyzing WBC and its types for structure and shape is quite challenging. To aid in hematology, this work provides classification of WBC classification based on modified U‐Net and support vector machines (SVM). A modified U‐Net architecture is developed to segment WBC followed by feature extraction and classification by radial basis function‐support vector machine (RBF‐SVM). Experiments indicated that the modified U Net segmentation can detect the WBC nucleus with a dice similarity coefficient of 0.972. The proposed U‐Net‐SVM can recognize WBCs in Raabin‐WBC, LISC, and BCCD datasets with an accuracy of 99.45%, 98.62%, and 98.81%, respectively. Further investigation on leukemia dataset, ALL‐IDB2, revealed an accuracy of 99.42% with 100% sensitivity and specificity. The proposed model can be used to investigate WBCs and hence provide a great support to the hematologists in analyzing the blood smear for various disease identifications.

Abstract This paper proposes a novel and robust predictive method using modified spider monkey optimization (MSMO) and probabilistic neural network (PNN) for face recognition. The limitation of the traditional spider monkey optimization (SMO) approach to obtaining an optimal solution for classification problems is overcome by enhancing the performance of SMO by modifying the perturbation rate with a non‐linear function, thereby improving the convergence of SMO. The framework comprises image preprocessing, feature extraction using dual tree complex wavelet transform (DT‐CWT), feature selection using the modified spider monkey optimization algorithm (MSMO), and classification using PNN. The proposed method is tested on the Yale and AR Face datasets. Experimental outcomes reveal that the proposed framework attain an accuracy of 99.4% with appreciable sensitivity, specificity, and G‐mean. To examine the efficacy of MSMO, parametric studies are conducted, which showed that MSMO converges faster with high fitness when compared to similar evolutionary algorithms like Genetic Algorithm (GA), Grey Wolf Optimization Algorithm (GWO), Particle Swarm Algorithm (PSO), and Cuckoo Search (CS) in selecting the optimal feature set. The MSMO‐PNN method outperforms similar state‐of‐the‐art methods, which reveals that the method proposed is competitive. The proposed model is robust to Gaussian and salt–pepper noise, obtaining the highest accuracy of 97.89% for varied noise density and variance.

Abstract Osteoarthritis (OA) damages the articular cartilage of the knee and is a severe degenerative joint condition. Currently, OA diagnosis is carried out by symptom analysis and progressive evaluation of the radiographs, although the method is subjective. This work presents a novel computer aided diagnostic system to diagnose the severity of the disease in its early stages. The proposed method comprises of stages that include image pre‐processing, extraction of features based on discrete wavelet decomposition, histogram, GLCM and texture features, and classification by stacked up‐RBM Deep Belief Networks (Hybrid DBN) finally. The performance of HDBN is improved by optimizing the hyper parameters with the Salp Swarm Optimization Algorithm (SSA). Experiments conducted on the Kaggle database consisting of 7503 images demonstrated an overall accuracy of 99.45% with 100% sensitivity and specificity. The impact on isolated and combined feature contributions is also analyzed using 10‐fold cross validation (CV). The robustness of the algorithm is tested on degraded images of salt‐pepper, Gaussian, and Poisson noise, which proved the effectiveness of the method. Comparison of the method proposed with similar techniques is done by employing different optimization algorithms and classifiers.

Inverse dynamics is a commonly used technique in the control of dynamic systems. A fuzzy inverse dynamics controller (feedforward control) for a Pneumatic Muscle system is designed using two methods: Weighted Average and the Least Squared Error. Both methods use data collected from the system. The inverse model obtained using the two methods are tested for its trajectory tracking capabilities and the results are compared. Simulation results show that the feedforward controller obtained using Least Squared Error method has better trajectory tracking capabilities than the weighted average scheme.