A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

Support vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in compa

Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.

Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are

Linear discriminant analysis and logistic regression are the most widely used in multivariate statistical methods for analysis of data with categorical outcome variables .Both of them are appropriate for the development of linear  classification models .linear discriminant analysis has been that the data of explanatory variables must be distributed multivariate normal distribution. While logistic regression no assumptions on the distribution of the explanatory data. Hence ,It is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions.

In this paper we have been focus for the comparison between three forms for classification data belongs

In this search, a new bioluminescent technique was proved for pyrophosphate which was employed to single- nucleotide polymorphism (SNP) diagnosis using one-base extension reaction. Four Mycobacterium tuberculosis genes were chosen (Rpob, InhA, KatG, GyrA) genes. Fifty-four specimens were used in this study fifty-three proved as drug-resistant specimens by The Iraqi Institute of Chest and Respiratory Diseases in Baghdad., also one specimen was used as a negative control. The procedure of this assay was as follows. A specific primer within each aliquot owning a short 3-OH end of the base of the target gene was hybridized to the single-stranded DNA template. Then, (exo-) Klenow DNA polymerase and one of either ?-thio-dATP, dTTP, dGTP, or dCTP

This study aims to use claystone beds exposed in the Injana Formation (Late Miocene) at Karbala-Najaf plateau, middle of Iraq for the manufacturing of perforated and ordinary bricks. The claystone samples were assessed as an alternative material of the recent sediments, which are preferred to remain as agricultural land. The claystones are sandy mud composing of 29.1 - 39.1% clay, 37.2 - 54.8% silt and 14.1-26.8% sand. They consist of kaolinite, illite, chlorite, palygorskite, and montmorillonite with a lot of quartz, calcite, dolomite, gypsum and feldspar. Claystone samples were characterized by linear shrinkage 0.01 - 0.1%, volume shrinkage 0.1 - 0.9%, bulk density 1.2 - 2.11gm/cm3 (1.68 g / cm3 average), and the efflorescence is

Optical burst switching (OBS) network is a new generation optical communication technology. In an OBS network, an edge node first sends a control packet, called burst header packet (BHP) which reserves the necessary resources for the upcoming data burst (DB). Once the reservation is complete, the DB starts travelling to its destination through the reserved path. A notable attack on OBS network is BHP flooding attack where an edge node sends BHPs to reserve resources, but never actually sends the associated DB. As a result the reserved resources are wasted and when this happen in sufficiently large scale, a denial of service (DoS) may take place. In this study, we propose a semi-supervised machine learning approach using k-means algorithm

This study evaluated the effect of two different types of amino group-containing pharmacological inhibitors on titanium corrosion under acidic conditions. The first inhibitor examined was the antibiotic trimethoprim. The second inhibitor studied was the nonsteroidal anti-inflammatory drug voltaren. The results showed that titanium had a high corrosion current in the absence of a corrosion inhibitor, however, trimethoprim notably enhanced corrosion resistance and protection percentages of titanium (%IE) at higher concentrations by adhering to the metal surface, as evidenced by improved protection percentages. Furthermore, the results showed that as Voltaren concentration was increased, both corrosion resistance and inhibition efficie