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Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review
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Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze medical images with favorable results. It can help save lives faster and rectify some medical errors. In this study, we look at the most up-to-date methodologies for medical image analytics that use convolutional neural networks on MRI images. There are several approaches to diagnosing and classifying brain cancers. Inside the brain, irregular cells grow so that a brain tumor appears. The size of the tumor and the part of the brain affected impact the symptoms.

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Publication Date
Sun Dec 30 2018
Journal Name
Journal Of Engineering
A Cognition Path Planning with a Nonlinear Controller Design for Wheeled Mobile Robot Based on an Intelligent Algorithm
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This paper presents a cognition path planning with control algorithm design for a nonholonomic wheeled mobile robot based on Particle Swarm Optimization (PSO) algorithm. The aim of this work is to propose the circular roadmap (CRM) method to plan and generate optimal path with free navigation as well as to propose a nonlinear MIMO-PID-MENN controller in order to track the wheeled mobile robot on the reference path. The PSO is used to find an online tune the control parameters of the proposed controller to get the best torques actions for the wheeled mobile robot. The numerical simulation results based on the Matlab package show that the proposed structure has a precise and highly accurate distance of the generated refere

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Publication Date
Mon Jan 01 2024
Journal Name
Scripta Medica
The value of dynamic contrast-enhanced MRI and diffusion-weighted sequence in the evaluation of endometrial lesions
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Background/Aim: Endometrial abnormalities represent a diagnostic challenge due to overlapping imaging features with normal endometrium. Aim of this study was to assess accuracy of dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging (MRI) in evaluation of endometrial lesions in comparison with T2 and to assess local staging validity and degree of myometrial invasion in malignancy. Methods: Forty patients with abnormal vaginal bleeding or sonographic thickened endometrial were recruited. MRI examination of pelvis was per-formed using 1.5 T scanner with a pelvic array coil. Conventional T1-and T2, dynamic contrast-enhanced (DCE) sequences and diffusion-weighted image (DWI) were performed. Results: Mean age of pa

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Publication Date
Mon Jun 01 2015
Journal Name
The European Physical Journal Applied Physics
A carbon nanotubes photoconductive detector for middle and far infrared regions based on porous silicon and a polyamide nylon polymer
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Publication Date
Mon Jan 20 2025
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Assessing Landsat Processing Levels and Support Vector Machine Classification
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The availability of different processing levels for satellite images makes it important to measure their suitability for classification tasks. This study investigates the impact of the Landsat data processing level on the accuracy of land cover classification using a support vector machine (SVM) classifier. The classification accuracy values of Landsat 8 (LS8) and Landsat 9 (LS9) data at different processing levels vary notably. For LS9, Collection 2 Level 2 (C2L2) achieved the highest accuracy of (86.55%) with the polynomial kernel of the SVM classifier, surpassing the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) at (85.31%) and Collection 2 Level 1 (C2L1) at (84.93%). The LS8 data exhibits similar behavior. Conv

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Publication Date
Thu Jan 01 2015
Journal Name
Energy Sources, Part A: Recovery, Utilization, And Environmental Effects
Ultra Deep Hydrotreatment of Iraqi Vacuum Gas Oil Using a Modified Catalyst
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A set of hydro treating experiments are carried out on vacuum gas oil in a trickle bed reactor to study the hydrodesulfurization and hydrodenitrogenation based on two model compounds, carbazole (non-basic nitrogen compound) and acridine (basic nitrogen compound), which are added at 0–200 ppm to the tested oil, and dibenzotiophene is used as a sulfur model compound at 3,000 ppm over commercial CoMo/ Al2O3 and prepared PtMo/Al2O3. The impregnation method is used to prepare (0.5% Pt) PtMo/Al2O3. The basic sites are found to be very small, and the two catalysts exhibit good metal support interaction. In the absence of nitrogen compounds over the tested catalysts in the trickle bed reactor at temperatures of 523 to 573 K, liquid hourly space v

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Publication Date
Tue Apr 30 2024
Journal Name
Iraqi Journal Of Science
Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network
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     Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certai

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Publication Date
Mon Apr 03 2023
Journal Name
Journal Of Educational And Psychological Researches
The Effectiveness of a Training Program Based on Connectivism Theory in Developing E-Learning Competencies among Teachers of Islamic Education in Dhofar Governorate
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Abstract

The study aims to build a training program based on the Connectivism Theory to develop e-learning competencies for Islamic education teachers in the Governorate of Dhofar, as well as to identify its effectiveness. The study sample consisted of (30) Islamic education teachers to implement the training program, they were randomly selected. The study used the descriptive approach to determine the electronic competencies and build the training program, and the quasi-experimental approach to determine the effectiveness of the program. The study tools were the cognitive achievement test and the observation card, which were applied before and after. The study found that the effectiveness of the training program

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Publication Date
Fri Sep 02 2022
Journal Name
Military Medical Science Letters
SOLID LIPID NANOPARTICLES AS A PROMISING APPROACH FOR DELIVERY OF ANTICANCER AGENTS: REVIEW ARTICLE
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Publication Date
Fri Sep 02 2022
Journal Name
Military Medical Science Letters
SOLID LIPID NANOPARTICLES AS A PROMISING APPROACH FOR DELIVERY OF ANTICANCER AGENTS: REVIEW ARTICLE
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Cancer disease has a complicated pathophysiology and is one of the major causes of death and morbidity. Classical cancer therapies include chemotherapy, radiation therapy, and immunotherapy. A typical treatment is chemotherapy, which delivers cytotoxic medications to patients to suppress the uncontrolled growth of cancerous cells. Conventional oral medication has a number of drawbacks, including a lack of selectivity, cytotoxicity, and multi-drug resistance, all of which offer significant obstacles to effective cancer treatment. Multidrug resistance (MDR) remains a major challenge for effective cancer chemotherapeutic interventions. The advent of nanotechnology approach has developed the field of tumor diagnosis and treatment. Cancer nanote

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Publication Date
Tue Dec 01 2020
Journal Name
Baghdad Science Journal
Emergency Messages Dissemination Challenges Through Connected Vehicles for Efficient Intelligent Transportation Systems: A Review
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Recent growth in transport and wireless communication technologies has aided the evolution of Intelligent Transportation Systems (ITS). The ITS is based on different types of transportation modes like road, rail, ocean and aviation. Vehicular ad hoc network (VANET) is a technology that considers moving vehicles as nodes in a network to create a wireless communication network. VANET has emerged as a resourceful approach to enhance the road safety. Road safety has become a critical issue in recent years. Emergency incidents such as accidents, heavy traffic and road damages are the main causes of the inefficiency of the traffic flow. These occurrences do not only create the congestion on the road but also increase the fuel consumption and p

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