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Multiplicative speckle is a dominant type of noise that spoils the inherent features of the medical ultrasound (US) images. Apart from the speckle, impulse and Gaussian noises also appear in the US image due to the error encountered during the data transmission and transition of switching circuits and sensors. The noise not only deteriorates the visual quality of the US but also creates complications in the diagnosis. In this study, an adaptive comprehensive particle swarm optimisation‐based functional‐link neural network (ACPSO‐FLNN) filtre has been proposed and implemented in filtering noisy US images in different noise conditions. The proposed filtre is compared with some state‐of‐the‐art filtering techniques. Quantitative and qualitative measures such as training time, time complexity, convergence rate, and statistical test are included to study the performance of the proposed filtre. Furthermore, sensitivity, computational complexity, and order of the proposed filtre are also investigated. Friedman's test with 50 images is performed for statistical validation. The lower rank, that is, 6 and critical value of 21 × 10–4 of the proposed ACPSO‐FLNN filtre validates its dominance over other filtres.

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: A serious problem in Wireless Sensor Networks (WSNs) is to attain high-energy efficiency as battery is used to power and have limited stored energy. They can’t be suitably replaced or recharged. Appearance of renewable energy harvesting techniques and their combination with sensor devices gives Energy Harvesting Wireless Sensor Networks (EHWSNs). IoT is now becoming part of our lives, comforting simplifying our routines and work life. IoT is very popular . It connects together, computes, communicates and performs the required task. IoT is actually a network of physical devices or things that can interact with each other to share information. This paper gives an overview of WSN and IoT, related work, different ways of connecting WSN with internet, development of smart home, challenges for WSN etc. Next a Framework for performance optimization in IoT is given and QC-PC-MCSC heuristic is analyzed in terms of Energy Efficiency and Life Time of a sensor on Energy Latency Density Design Space, a topology management application that is power efficient. QC-PC-MCSC and QC-MCSC are compared for Energy Efficiency and Life Time of a sensor over energy latency density design space, a topology management application.

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: VANET is an application used for the intelligent transportation system which improves traffic safety as well as its efficiency. We have reviewed the patents related to vehicular Ad-Hoc Network and their issue. To avoid road accidents a lot of information we need in advance. This paper has developed a framework which minimizes the possibilities of the black hole attack in VANET. According to us, there are two possible solutions for this purpose. The first is to see alternative routes for the same destination. The second compromises of exploiting the packet header's packet sequence number which is always included in each packet header. The second procedure is able to verify that 72% to 96% of route which is discovered depends on pause time t which is the minimum time for delay in the packet transition in the network when AODV routing protocol is used for packet transitions. The framework is analyzed for the possible attacks by the Black Hole, and Gray Hole attacks and also effects of the attacks are recorded and studied by practically using it. A secured VANET is essential for the future of the network. Currently acquiring this network will boost the possibility of VANET to develop and reduce the time of its implementation in the real world scenarios. In this work it is concluded that both attacks can be implemented and detected over the network apart from of the fact that both attacks are categories differently.