The rapid growth of the Internet of Things (IoT) has led to dense wireless sensor networks (WSNs) deployed in critical applications such as smart cities, industrial monitoring, and healthcare. However, energy constraints, unpredictable communication delays, and inefficient data aggregation remain significant challenges that limit network reliability and operational lifespan. Traditional approaches often fail to balance delay minimization with energy efficiency, especially in large-scale or dynamic networks. To address these issues, this study proposes CTSO-DRNN, a novel framework that integrates Chronological Tangent Search Optimization (CTSO) with a Deep Recurrent Neural Network (DRNN) for accurate delay prediction and optimized data aggregation. The framework constructs Link Delay-Distance (LDD) trees to guide hierarchical communication and leverages CTSO to optimize the DRNN for predicting network delays, enabling adaptive scheduling and energy-aware operation. Experimental findings from simulated WSNs comprising 100 to 250 nodes indicate that the CTSO-DRNN approach decreases the average communication delay by roughly 28% to 60%, increases link lifetime by 8% to 30%, and reduces routing distance by 14% to 25% when compared to various leading-edge techniques across diverse network densities. These improvements highlight the framework’s ability to maintain low latency, prolong network operation, and enhance overall energy efficiency.

Timely and accurate diagnosis of skin diseases is crucial as conventional methods are time-consuming and prone to errors. Traditional trial-and-error approaches often aggregate multiple models without optimization by resulting in suboptimal performance. To address these challenges, we propose a novel Squid Game Optimization-Dimension Reduction-based Ensemble (SGO-DRE) method for the precise diagnosis of skin diseases. Our approach begins by selecting pre-trained models named MobileNetV1, DenseNet201, and Xception for robust feature extraction. These models are enhanced with dimension reduction blocks to improve efficiency. To tackle the aggregation problem of various models, we leverage the Squid Game Optimization (SGO) algorithm, which iteratively searches for the optimal weightage set to assign the appropriate weightage to each individual model within the proposed weighted average aggregation ensemble approach. The proposed ensemble method effectively utilizes the strengths of each model. We evaluated the proposed method using an 8-class skin disease dataset, a 6-class MSLD dataset, and a 4-class MSID dataset, achieving accuracies of 98.71%, 96.34%, and 93.46%, respectively. Additionally, we employed visual tools like Grad-CAM, ROC curves, and Precision-Recall curves to interpret the decision making of models and assess its performance. These evaluations ensure that the proposed method not only provides robust results but also enhances interpretability and reliability in clinical decision-making.

A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. Even if intercepted, AABHE makes sure that sensitive data cannot be accessed by unauthorized parties. With a focus on protecting huge files in HDFS, the suggested approach achieves 98% security robustness and 95% encryption efficiency, outperforming other encryption methods including Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Key-Policy Attribute-Based Encryption (KB-ABE), and Advanced Encryption Standard combined with Attribute-Based Encryption (AES+ABE). By fixing Hadoop’s security flaws, AABHE fortifies its protections against data breaches and enhances Hadoop’s dependability as a platform for processing and storing massive amounts of data.