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In the last few decades, poor prognosis of pancreatic tumor has been an issue of concern in spite of the recent advancements in the different imaging modalities. Small size, similar attenuation to normal sized pancreas or concealed position of pancreas during CT scans are the factors that leads to failure in early diagnosis of pancreatic tumor. In this research, an organized framework is proposed for monitoring, classifying and diagnosing of pancreatic tumor. The suggested model integrates the technique of Deep Neural Network (DNN) and optimistic aspects of nature-inspired algorithms; this model aims to achieve a harmonious combination of both the techniques. The proposed model examines the medical images obtained from CT scans for the presence of pancreatic tumor using SSA-ML image segmentation on CT dataset. Evaluation of suggested model in comparison to other contemporary models IDLDMS, ODL, weighted KLM, Kernel-ELM, and ELM models is also performed in reference to sensitivity, specificity, accuracy and F1 score. The classification accuracy of our model is 99.44 % which reflects its supremacy over other recent models.

Advancing the efficiency and reliability of wireless sensor networks is a paramount pursuit in modern networking research. In this context, we introduce a groundbreaking approach based on Hidden Markov Chain (HMC) modeling with opportunistic routing, harnessed by the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism. Our innovative algorithm addresses key challenges in Wireless Body Area Networks, including delay, throughput, and bandwidth utilization. By strategically integrating the HMC model, which captures intricate state transitions and emission probabilities, our proposed method introduces a robust solution for optimizing node connections and routing decisions. This fusion of HMC and opportunistic routing capitalizes on the strengths of both paradigms, enhancing the network's ability to make intelligent decisions in dynamic scenarios. Through rigorous simulations, we showcase the algorithm's prowess in achieving efficient data transmission. The empirical evidence from these simulations underscores the algorithm's superiority when juxtaposed against state-of-the-art mechanisms such as Simple Multi-Packet Access (SMPA) and Priority-based Congestion-avoidance Routing Protocol (PCRP). Our algorithm not only outperforms existing solutions in terms of delay, throughput, and bandwidth utilization, but it also showcases its ability to adapt to varying network conditions, making it a versatile tool for diverse applications.

Abstract This article studies a distributed consensus‐based estimation problem for discrete time‐varying non‐linear systems with missing measurements and Denial of Service (DoS) attacks. The probability of missing measurements is independent for each sensor. The communication link between sensor nodes is unreliable and subjected to DoS attacks. To achieve accurate state estimation against missing measurements, a local estimator with compensation mechanism is designed for each sensor node. A stochastic event‐triggered mechanism is used to lessen additional information transfer. Based on this, a distributed consensus‐based estimator is constructed by continually fusing local neighbours information matrixs and vectors. Moreover, the analysis of the designed estimator boundedness is presented. Finally, the effectiveness of the proposed algorithm is verified by three numerical examples.