Stimulant abuse enhances dopamine release, thereby causing increased excitation. Any extent of stimulant abuse can considerably harm the user. Thus, methods of detecting stimulants must be precise, accurate, and reliable. A novel terahertz (THz) photonic crystal fiber with a Topas substrate is designed and rigorously investigated for detecting liquid amphetamine, cocaine, and ketamine. The fiber structure has a pentagonal shape and comprises circular air holes in the core and cladding spatial extents. As shown in finite element simulation, the proposed fiber yields a high relative sensitivity of approximately 80 % when any of the liquid stimulants is infiltrated in the core air holes. At 1 THz operating frequency, the proposed fiber produces a large effective mode area, negligible confinement loss, and extremely low bending and effective material losses. Other THz waveguiding properties, such as core power fraction and total loss, are also studied. Lastly, a positive and negative 2 % fabrication tolerance is set to ensure seamless potential practical realization of the fiber.

The complexity of raw functional magnetic resonance imaging (fMRI) data with artifacts leads to significant challenges in multioperations with these data. FMRI data analysis is extensively used in neuroimaging fields, but the tools for processing fMRI data are lacking. A novel APP DESIGNER conversion, preprocessing, and postprocessing of fMRI (CPREPP fMRI) tool is proposed and developed in this work. This toolbox is intended for pipeline fMRI data analysis, including full analysis of fMRI data, starting from DICOM conversion, then checking the quality of data at each step, and ending in postprocessing analysis. The CPREPP fMRI tool includes 12 conversions of scientific processes that reflect all conversion possibilities among them. In addition, specific preprocessing order steps are proposed on the basis of data acquisition mode (interleaved and sequential modes). A severe and crucial comparison between statistical parametric and nonparametric mapping approaches of second-level analysis is presented in the same tool. The CPREPP fMRI tool can provide reports to exclude subjects with the extreme movement of the head during the scan, and a range of fMRI images are generated to verify the normalization effect easily. Real fMRI data are used in this work to prepare fMRI data tests. The experiment stimuli are chewing and biting, and the data are acquired from the National Magnetic Resonance Research (UMRAM) Center in Ankara, Turkey. A free dataset is used to compare the methods for postprocessing fMRI tests.

This paper deals with the classification of the kidneys for renal stones on ultrasound images. Convolutional neural network (CNN) and pre-trained CNN (VGG16) models are used to extract features from ultrasound images. Extreme gradient boosting (XGBoost) classifiers and random forests are used for classification. The features extracted from CNN and VGG16 are used to compare the performance of XGBoost and random forest. An image with normal and renal stones was classified. This work uses 630 real ultrasound images from Al-Diwaniyah General Teaching Hospital (a lithotripsy center) in Iraq. Classifier performance is evaluated using its accuracy, recall, and F1 score. With an accuracy of 99.47%, CNN-XGBoost is the most accurate model.

Breast cancer is one of the greatest frequent tumours among females in Iraq. Medical ultrasound imaging has become a common modality for breast tumour imaging because of its ease of use, low cost, and safety. In the present study, Convolutional Neural Network (CNN) feature extraction approaches were used to classify breast ultrasound imaging. The CNN model used is composed of four-layer for breast cancer ultrasound image analysis. Two types of free datasets were used. These data were divided into groups A and B. Group A has three classes, namely benign, malignant and normal, while group B has two classes, namely, benign and malignant. The proposed technique was assessed based on its accuracy, precision, F1 score and recall. The model's classification accuracy for data A was 96%, whereas for data B was 100%.