The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices

 A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.

A calamitic symmetric liquid crystalline consisting of an azo group containing 5H-Thiazolo[3,4-b][1,3,4]thiadiazole moiety compound[III] was synthesized via sequence reactions starting from reaction terephthaldehyde with mercaptoacetic acid and thiosemicarbazide in the presence of concentrated sulfuric acid to synthesized 5,5'-(1,4-phenylene)bis(5Hthiazolo[4,3-b][1,3,4]thiadiazol-2-amine)[I] then the azo compound [II] synthesized by coupling between diazonium salt of the compound [I] with phenol at (0-4) ̊C., after that the compound [III] was synthesized by the reaction of the compound [II] with methyl bromide in alkaline media. The compounds are characterized by melting points, FTIR and 1HNMR spectroscopy. The mesomorphic behavior was stu

Surface water samples from different locations within Tigris River's boundaries in Baghdad city have been analyzed for drinking purposes. Correlation coefficients among different parameters were determined. An attempt has been made to develop linear regression equations to predict the concentration of water quality constituents having significant correlation coefficients with electrical conductivity (EC). This study aims to find five regression models produced and validated using electrical conductivity as a predictor to predict total hardness (TH), calcium (Ca), chloride (Cl), sulfate (SO4), and total dissolved solids (TDS). The five models showed good/excellent prediction ability of the parameters mentioned above, which is a very