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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]