Rapid Internet use growth and applications of diverse military have managed researchers to develop smart systems to help applications and users achieve the facilities through the provision of required service quality in networks. Any smart technologies offer protection in interactions in dispersed locations such as, e-commerce, mobile networking, telecommunications and management of network. Furthermore, this article proposed on intelligent feature selection methods and intrusion detection (ISTID) organization in webs based on neuron-genetic algorithms, intelligent software agents, genetic algorithms, particulate swarm intelligence and neural networks, rough-set. These techniques were useful to identify and prevent network intrusion to provide Internet safety and improve service value and accuracy, performance and efficiency. Furthermore, new algorithms of intelligent rules-based attributes collection algorithm for efficient function and rules-based improved vector support computer, were proposed in this article, along with a survey into the current smart techniques for intrusion detection systems.

Reliability, resilience, and vulnerability are the traditional risk measures used to assess the performance of a reservoir system. Among these measures, resilience is used to assess the ability of a reservoir system to recover from a failure event. However, the time‐independent static resilience does not consider the system characteristics, interaction of various individual components and does not provide much insight into reservoir performance from the beginning of the failure event until the full performance recovery. Knowledge of dynamic reservoir behavior under the disturbance offers opportunities for proactive and/or reactive adaptive response that can be selected to maximize reservoir resilience. A novel measure is required to provide insight into the dynamics of reservoir performance based on the reservoir system characteristics and its adaptive capacity. The reservoir system characteristics include, among others, reservoir storage curve, reservoir inflow, reservoir outflow capacity, and reservoir operating rules. The reservoir adaptive capacity can be expressed using various impacts of reservoir performance under the disturbance (like reservoir release for meeting a particular demand, socioeconomic consequences of reservoir performance, or resulting environmental state of the river upstream and downstream from the reservoir). Another way of expressing reservoir adaptive capacity to a disturbing event may include aggregated measures like reservoir robustness, redundancy, resourcefulness, and rapidity. A novel measure that combines reservoir performance and its adaptive capacity is proposed in this paper and named “dynamic resilience.” The paper also proposes a generic simulation methodology for quantifying reservoir resilience as a function of time. The proposed resilience measure is applied to a single multipurpose reservoir operation and tested for a set of failure scenarios. The dynamic behavior of reservoir resilience is captured using the system dynamics simulation approach, a feedback‐based object‐oriented method, very effective for modeling complex systems. The results of dynamic resilience are compared with the traditional performance measures in order to identify advantages of the proposed measure. The results confirm that the dynamic resilience is a powerful tool for selecting proactive and reactive adaptive response of a multipurpose reservoir to a disturbing event that cannot be achieved using traditional measures. The generic quantification approach proposed in the paper allows for easy use of dynamic resilience for planning and operations of various civil infrastructure systems.

In this paper, a photonic crystal ring resonator based bio sensor is designed to sense different blood constituents in blood in the wavelength range of 1530 nm-1615 nm for biomedical applications. The blood constituents such as hemoglobin white blood cell, red blood cell, blood sugar, blood urea, albumin, serum bilirubin direct, and ammonia are sensed for the corresponding transmission output power, Q factor, and refractive index changes. As the blood constituent has unique refractive index, the resonant wavelength and output power are varied from one to another, which are used to identify the blood constituents.

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