Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes

     This research Sought to identify the correlation relationships and the impact of each of the job description and perceived organizational support, Excellent Job performance of the heads of academic departments in the faculties of the University of Sulaymaniyah Iraqi Kurdistan Region, totaling (89) as President, and to achieve this was Default plan includes research variables as well as the formulation of a number of preparation fundamental assumptions, and researchers used a questionnaire for this purpose as a tool head of the collection of data and information, as it was distributed (80) copies, and the number of retrieved them (76) a copy of a valid statistical analysis, as well as conducting personal i

In this study, an analysis of re-using the JPEG lossy algorithm on the quality of satellite imagery is presented. The standard JPEG compression algorithm is adopted and applied using Irfan view program, the rang of JPEG quality that used is 50-100.Depending on the calculated satellite image quality variation, the maximum number of the re-use of the JPEG lossy algorithm adopted in this study is 50 times. The image quality degradation to the JPEG quality factor and the number of re-use of the JPEG algorithm to store the satellite image is analyzed.

Background:sThe aims of this study were to evaluate and compare the ability of three different techniques to obdurate simulated lateral canals, evaluate the effect of the main canal curvature on obturation of lateral canals and compare the gutta-percha penetration between coronal and apical lateral canals. Materials and methods: Resin blocks with 30 straight and 30 curved were used in this study. Each canal has two parallel lateral canals. The main canal has 0.3 mm apical diameter and 0.04 taper. The canals were divided into six groups according to canal curvature and obturation techniques used (n=10): Groups C1 and C2: straight and curved canals obturated with continuous wave technique using E&Q masterTM system. Groups O1 and O2: straight

Background: Decontamination of gutta percha cones was important factor for success of root canal treatment. The aim of the present in vitro study was to identify and to compare the antimicrobial effect of following disinfection solutions: 0.2% chlorhexidine gluconate, Iodine, tetracycline hydrochloride solution, EDTA & formocresol mixed with zinc oxide eugenol, on E faecalis, E coli and Candida albicans using sensitivity test Materials and Methods: Three types of microorganisms were isolated from infected root canals (E faecalis, E coli and Candida albicans) and cultured on Mueller Hinton agar petri-dishes. Disinfection of gutta percha cones done by immersion in six disinfection solutions (six groups), the groups are: distill water (used a

This abstract focuses on the significance of wireless body area networks (WBANs) as a cutting-edge and self-governing technology, which has garnered substantial attention from researchers. The central challenge faced by WBANs revolves around upholding quality of service (QoS) within rapidly evolving sectors like healthcare. The intricate task of managing diverse traffic types with limited resources further compounds this challenge. Particularly in medical WBANs, the prioritization of vital data is crucial to ensure prompt delivery of critical information. Given the stringent requirements of these systems, any data loss or delays are untenable, necessitating the implementation of intelligent algorithms. These algorithms play a pivota

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the appl