The rapid development of Internet of Things (IoT) devices and their increasing numbers have caused a tremendous increase in network traffic and a wider range of cyber-attacks. This growing trend has complicated the detection process for traditional intrusion detection systems and heightened the challenges faced by these devices, such as imbalanced and large training data. This study presents a cohesive methodology of a series of intelligent techniques to prepare clean and balanced data for training the first (core) layer of a robust hierarchical intrusion detection system. The methodology was built by cleaning and compressing the data using an Autoencoder and preparing a strong latent space for balancing using a hybrid method that combines

The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat

Sewage water is a mixture of water and solids added to water for various uses, so it needs to be treated to meet local or global standards for environmentally friendly waste production. The present study aimed to analyze the new Maaymyrh sewage treatment plant's quality parameters statistically at Hilla city. The plant is designed to serve 500,000 populations, and it is operating on a biological treatment method (Activated Sludge Process) with an average wastewater inflow of 107,000m³/day. Wastewater data were collected daily by the Mayoralty of Hilla from November 2019 to June 2020 from the influent and effluent in the (STP) new in Maaymyrh for five water quality standards, such as (BOD5), (COD), (TSS), (TP)

The need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2,0,0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlation coefficien

Artificial Neural Network (ANN) model's application is widely increased for wastewater treatment plant (WWTP) variables prediction and forecasting which can enable the operators to take appropriate action and maintaining the norms. It is much easier modeling tool for dealing with complex nature WWTP modeling comparing with other traditional mathematical models. ANN technique significance has been considered at present study for the prediction of sequencing batch reactor (SBR) performance based on effluent's (BOD5/COD) ratio after collecting the required historical daily SBR data for two years operation (2015-2016) from Baghdad Mayoralty and Al-Rustamiya WWTP office, Iraq. The prediction was gotten by the application of a feed-forwa

This paper describes the transport of Alkaloids through Rotating Discs Contactor (RDC) using n-decane as a liquid membrane. The transport of Pelletierine Alkaloid from a source phase through bulk liquid membrane to the receiving phase has been investigated. The general behaviour of Pertraction process indicates that% Extraction of pelletierine Alkaloid increased with increase in the number of stages and the agitation speed but high agitation speed was not favoured due to the increased risk of droplet formation during the operation. The pH of source and receiving phases were also investigated. The effect of organic solvent membrane on the extraction of Pelletierine was evaluated using ndecane, n-hexane and methyl cyclohexane. The results sho

The present work aimed to study the efficiency of nanofiltration (NF) and reverse osmosis (RO) process for treatment of heavy metals wastewater contains zinc. In this research, the salt of heavy metals were zinc chloride (ZnCl2) used as feed solution.Nanofiltration and reverse osmosis membranes are made from polyamide as spiral wound module. The parameters studied were: operating time (0 – 70 min), feed concentrations for zinc ions (10 – 300 mg/l), operating pressure (1 – 4 bar).The theoretical results showed, flux of water through membrane decline from 19 to 10.85 LMH with time. Flux decrease from 25.84 to 10.88 LMH with the increment of feed concentration. The raise of pressure, the flux increase for NF and RO membranes.The maximum

AlPO4 catalysts supported with WO3 were prepared by impregnating the catalysts with ammonium metatungstate. The catalysts were checked by X-ray Diffraction (XRD), AFM, and SEM; also, the catalysts analysis was done by X-Ray (EDX). Finally, the N2 adsorption-desorption was used to measure the pore volume and surface area of the catalyst. The prepared catalyst has a surface area of 185.83 m2/g, pore volume of 0.645 cm3/g at a calcination temperature of 500°C for 3 hrs, and particle size of AlPO4 with an average of 35.36 nm. Transesterification of edible oil using WO3/AlPO4 was performed, it was observed that WO3/AlPO4 catalysts give high conversion of edible oil, and this is attributed to the high surface area, smaller particle size, and the

The aim of this study is to synthesize an easy, non-toxic and eco-friendly method. Silver nanoparticles which were synthesized by leaf extract of mint were characterized by UV-Visible Spectroscopy which appears UVVisible spectrum of demonstrated a peak 448 nm corresponding to surface Plasmon resonance of silver nanoparticles, Fourier Transform Infrared Spectroscopy (FTIR); functional groups involved in the silver nanoparticles synthesis were identified, the presence of silver nanoparticles was confirmed by X-ray diffraction (XRD) and Atomic Force Microscope (AFM) analysis clearly illustrated that the shape of silver nanoparticles was spherical and the size of the silver nanoparticles has been measured as 55- 85 nm. Evaluation of its antimic