Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

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

In data transmission a change in single bit in the received data may lead to miss understanding or a disaster. Each bit in the sent information has high priority especially with information such as the address of the receiver. The importance of error detection with each single change is a key issue in data transmission field.
The ordinary single parity detection method can detect odd number of errors efficiently, but fails with even number of errors. Other detection methods such as two-dimensional and checksum showed better results and failed to cope with the increasing number of errors.
Two novel methods were suggested to detect the binary bit change errors when transmitting data in a noisy media.Those methods were: 2D-Checksum me

Submerged arc welding (SAW) process is an essential metal joining processes in industry. The quality of weld is a very important working aspect for the manufacturing and construction industries, the challenges are made optimal process environment. Design of experimental using Taguchi method (L9 orthogonal array (OA)) considering three SAW parameter are (welding current, arc voltage and welding speed) and three levels (300-350-400 Amp. , 32-36-40 V and 26-28-30 cm/min). The study was done on SAW process parameters on the mechanical properties of steel type comply with (ASTM A516 grade 70). Signal to Noise ratio (S/N) was computed to calculate the optimal process parameters. Percentage contributions of each parameter are validated by using an

Massive multiple-input multiple-output (massive-MIMO) is a promising technology for next generation wireless communications systems due to its capability to increase the data rate and meet the enormous ongoing data traffic explosion. However, in non-reciprocal channels, such as those encountered in frequency division duplex (FDD) systems, channel state information (CSI) estimation using downlink (DL) training sequence is to date very challenging issue, especially when the channel exhibits a shorter coherence time. In particular, the availability of sufficiently accurate CSI at the base transceiver station (BTS) allows an efficient precoding design in the DL transmission to be achieved, and thus, reliable communication systems can be obtaine

A new mixed ligand complexes were prepared by reaction of quinoline -2-carboxylic acid (L1) and 4,4?dimethyl-2,2?-bipyridyl (L2) with V(IV),Cr(III), Rh(III), Cd(II) and Pt(IV) ions. These complexes were isolated and characterized by (FT-IR) and (UV-Vis) spectroscopy, elemental analysis, flame atomic absorption technique, thermogravimetric analysis, in addition to magnetic susceptibility and conductivity measurements. Most complexes were mononuclear and with octahedral geometry, except Cd (II) with tetrahedral geometry, and V (IV) with square pyramidal geometry. A theoretical treatment of the ligands and the prepared complexes in gas phase was done using two programs Hyperchem.8 and Gaussian program (GaussView Currently Available Versions (

Two new organotin(IV) complexes Me2Snesc (C1) and Bu2Snesc (C2) have been synthesised from the reaction of the corresponding organotin(IV) chloride with the Schiff base ligand 3,4-dihydroxybenzaldehyde-4-ethylsemicarbazone (H2esc). The ligand was prepared in two steps. The first step includes the formation of 4-ethylsemicarbazide, which then reacted with 3,4-dihydroxybenzaldehyde to give the title ligand. Complex formation between the organotin(IV) moiety and the anionic form of 3,4-dihydroxybenzaldehy-4-ethylsemicarbazone occurred through the o-dihydroxy positions. The ligand and its complexes were characterised by elemental analysis, FT-IR and NMR (1H, 13C and 119Sn) spectroscopy. Accordingly, the complexes were proposed to have tetrahedr

In this research, the results of x-ray diffraction method were used to determine the uniform stress deformation and microstructure parameters of CuO nanoparticles to determine the lattice strain obtained and crystallite size and then to compare the results obtained by two model Halder Wagner and Size Strain Plot with the results of these methods of the same powder using equations during which the calculation of the size of the crystallite size and lattice strain, It was found that the results obtained the values of the crystallite size (19.81nm) and the lattice strain (0.004065) of the Halder-wagner model respectively and for the ssp method were the results of the crystallite size (17.20nm) and lattice strain (0.000305) respectively. The sa