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Summary: Many protocols, services, and electrical devices with built‐in sensors have been developed in response to the rapid expansion of the Internet of Things. Mobile ad hoc networks (MANETs) consist of a collection of autonomous mobile nodes that can form an ad hoc network in the absence of any pre‐existing infrastructure. System performance may suffer due to the changeable topology of MANETs. Since most mobile hosts operate on limited battery power, energy consumption poses the biggest challenge for MANETs. Both network lifetime and throughput improve when energy usage is reduced. However, existing approaches perform poorly in terms of energy efficiency. Scalability becomes a significant issue in large‐scale networks as they grow, leading to overhead associated with routing updates and maintenance that can become unmanageable. This article employs a MANET routing protocol combined with an energy conservation strategy. The clustering hierarchy is used in MANETs to maximize the network's lifespan, considering its limited energy resources. In the MANET communication process, the cluster head (CH) is selected using Fire Hawk Optimization (FHO). When choosing nodes to act as a cluster for an extended period, CH election factors in connectivity, mobility, and remaining energy. This process is achieved using an optimized version of the Ad hoc On‐Demand Distance Vector (AODV) routing protocol, utilizing Improved Chicken Swarm Optimization (ICSO). In comparison to existing protocols and optimization techniques, the proposed method offers an extended network lifespan ranging from 90 to 160 h and reduced energy consumption of 80 to 110 J, as indicated by the implementation results. [ABSTRACT FROM AUTHOR]

Many of the people now a days are suffering with thyroid disorder. Hence, the earlier detection of thyroid becomes very important in medical field to cure the patient.The detection methods are based on pattern recognition, machine learning and data mining. Thyroid disorder was caused by the imbalanced state of thyroid hormones, and is of two types, they are over production (hyperthyroidism) and less production (hypothyroidism). In this paper we applied machine learning models on the hypothyroid dataset taken from the UCI data repository to partially fulfill the detection of thyroid disorder. The experimental results have shown that, the proposed model willgive the good results [ABSTRACT FROM AUTHOR]

Summary: Mobile ad hoc networks (MANETs) become most vibrant and are widely used in various applications, including military, commercial sectors, and personal area networks. However, security is a major issue in routing; because of moving nodes, mainly it suffers from a black hole and gray hole attack. This kind of attack is difficult to predict and is harmful to the network. To address this problem, here, we introduce the gray wolf optimization (GWO) with trust setup data aggregation called gray wolf trust accumulation in wireless ad hoc network architecture. To maintain and enhance the security in routing, here, we update the trust setup data aggregation in GWO mechanism. After the detection of black hole and gray hole attack, we need to maintain the path stability for data transmission, trust schematic process monitor all the nodes, and maintain path stability. Using the behavior of GWO, we can detect the black hole and gray hole attack at a rate of 98.5% and also improve the packet delivery ratio by 98.2% with 10 m/s. Also, with the help of the trust setup data aggregation model, we can update our trust model and security maintenance. Thus, our proposed technique enhances the packet transmission in MANET and secures the routing layer. [ABSTRACT FROM AUTHOR]