Electrochemical machining is one of the widely used non-conventional machining processes to machine complex and difficult shapes for electrically conducting materials, such as super alloys, Ti-alloys, alloy steel, tool steel and stainless steel.  Use of optimal ECM process conditions can significantly reduce the ECM operating, tooling, and maintenance cost and can produce components with higher accuracy. This paper studies the effect of process parameters on surface roughness (Ra) and material removal rate (MRR), and the optimization of process conditions in ECM. Experiments were conducted based on Taguchi’s L9 orthogonal array (OA) with three process parameters viz. current, electrolyte concentration, and inter-electrode gap. Sig

This piece of research work aims to study one of the most difficult reaction and determination due to continuous and rapid variation of reaction products and the reactants. As molybdenum (VI) aid in the decomposition of hydrogen peroxide in alkaline medium of ammomia, thus means a continuous liberation of oxygen which cuases and in a continuous manner a distraction in the measurement process. On this basis pyrogallol was used to absorbe all liberated oxygen and the result is an a clean undisturbed signals. Molybdenum (VI) was determined in the range of 4-100 ?g.ml-1 with percentage linearity of 99.8% or (4-300 ?g.ml-1 with 94.4%) while L.O.D. was 3.5 ?g.ml-1. Interferring ions (cations and anions) were studied and their main effect was red

In this research, Argon gas was used to generate atmospheric plasma in the manufacture of platinum nanomaterials, to study the resultant plasma spectrum and to calculate the cellular toxicity of those manufactured nanomaterials. This research is keen on the generation of nonthermal atmospheric pressure plasma using aqueous platinum salts (H₂PtCl₆ 6H₂O) with different concentrations and exposure of cold plasma with a different time period used to produce platinum nanoparticles, to ensure typical preparation of nanoparticles. Visible UV and X-rays were performed for this purpose, and the diameter of the system probe was (1[Formula: see text]mm) with the Argon gas flow of

Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security

In this paper, the goal of proposed method is to protect data against different types of attacks by unauthorized parties. The basic idea of proposed method is generating a private key from a specific features of digital color image such as color (Red, Green and Blue); the generating process of private key from colors of digital color image performed via the computing process of color frequencies for blue color of an image then computing the maximum frequency of blue color, multiplying it by its number and adding process will performed to produce a generated key. After that the private key is generated, must be converting it into the binary representation form. The generated key is extracted from blue color of keyed image then we selects a c

Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B