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Self-Localization of Guide Robots Through Image Classification
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The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots.  To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accurate solution for indoor robot navigation. The more accurate solution of the guide robotic system opens a new window of the self-localization system and solves the more complex problem of indoor robot navigation. It makes a reliable interface between humans and robots. This study successfully demonstrated how a robot finds its initial position inside a room. A deep learning system, such as a convolutional neural network, trains the self-localization system as an image classification problem.  The robot was placed inside the room to collect images using a panoramic camera. Two datasets were created from the room images based on the height above and below the chest. The above-mentioned method achieved a localization accuracy of 98.98%.

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Publication Date
Wed Dec 08 2021
Journal Name
J. Inf. Hiding Multim. Signal Process.
Predication of Most Significant Features in Medical Image by Utilized CNN and Heatmap.
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The growth of developments in machine learning, the image processing methods along with availability of the medical imaging data are taking a big increase in the utilization of machine learning strategies in the medical area. The utilization of neural networks, mainly, in recent days, the convolutional neural networks (CNN), have powerful descriptors for computer added diagnosis systems. Even so, there are several issues when work with medical images in which many of medical images possess a low-quality noise-to-signal (NSR) ratio compared to scenes obtained with a digital camera, that generally qualified a confusingly low spatial resolution and tends to make the contrast between different tissues of body are very low and it difficult to co

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Publication Date
Sat Oct 01 2022
Journal Name
Baghdad Science Journal
Determination of Optimal Time-Average Wind Speed Data in the Southern Part of Malaysia
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Mersing is one of the places that have the potential for wind power development in Malaysia. Researchers often suggest it as an ideal place for generating electricity from wind power. However, before a location is chosen, several factors need to be considered. By analyzing the location ahead of time, resource waste can be avoided and maximum profitability to various parties can be realized. For this study, the focus is to identify the distribution of the wind speed of Mersing and to determine the optimal average of wind speed. This study is critical because the wind speed data for any region has its distribution. It changes daily and by season. Moreover, no determination has been made regarding selecting the average wind speed used for w

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Publication Date
Thu Dec 01 2022
Journal Name
Journal Of Engineering
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 med

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Publication Date
Fri Dec 01 2023
Journal Name
Al-khwarizmi Engineering Journal
Development of an ANN Model for RGB Color Classification using the Dataset Extracted from a Fabricated Colorimeter
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Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

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Publication Date
Fri Feb 08 2019
Journal Name
Journal Of The College Of Education For Women
Minimum Spanning Tree Algorithm for Skin Cancer Image Object Detection
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This paper proposes a new method Object Detection in Skin Cancer Image, the minimum
spanning tree Detection descriptor (MST). This ObjectDetection descriptor builds on the
structure of the minimum spanning tree constructed on the targettraining set of Skin Cancer
Images only. The Skin Cancer Image Detection of test objects relies on their distances to the
closest edge of thattree. Our experimentsshow that the Minimum Spanning Tree (MST) performs
especially well in case of Fogginessimage problems and in highNoisespaces for Skin Cancer
Image.
The proposed method of Object Detection Skin Cancer Image wasimplemented and tested on
different Skin Cancer Images. We obtained very good results . The experiment showed that

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Publication Date
Mon Jan 01 2024
Journal Name
Bio Web Of Conferences
An overview of machine learning classification techniques
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Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

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Publication Date
Sun Jul 02 2006
Journal Name
Journal Of Educational And Psychological Researches
التحكم الذاتي عند طلبة الجامعة وعلاقته بمستوى الطموح
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مشكلة البحث وأهمية:-

أن بناء الشخصية القادرة على التفاعل والتأثير في معطيات الحضارة ، والتقدم في مسالكها المتطورة ، بات هدفا لكل مجتمع يريد الوصول إلى مصاف الدول المتقدمة
(رسول ، 1984، ص7) وبالأخص في مجتمعنا الذي يمتلك مقومات المجتمعات المتحضرة ، فظهر الاهتمام بالدوافع التي تدفع الأفراد نحو مزيد من العطاء والإنجاز وتساعد في بناء شخصية قوية قادرة على تحقيق ما تصبو أليه من أهداف وطموحات ، ومتمتعة

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Publication Date
Tue Feb 14 2023
Journal Name
Journal Of Educational And Psychological Researches
Psychological Flow and its Association with Psychological Well-Being and Self –Management among a Sample of Fine Artists in Makkah Region Haneen Hussain Daggas
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Abstract

The research aims to examine the relationship between psychological flow, psychological well-being, and self-management among a sample of fine artists in the Makkah region and its governorates. The research also aims to examine the mean group differences in psychological flow, psychological well-being, and self-management due to demographic variables (sex and years of practicing arts). The sample consists of (110) male and female fine artists. The descriptive correlational approach was performed to collect the data by using the psychological flow scale developed by Payne et al (2011), which was translated by the researcher, the Oxford happiness questionnaire developed by Hills and Argyle (2002), it has t

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Publication Date
Mon Apr 01 2013
Journal Name
مجلة كلية بغداد للعلوم الاقتصادية الجامعة العدد الخاص بمؤتمر الكلية
A propose method for hiding image into image
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Publication Date
Mon Jan 01 2018
Journal Name
Journal Of The College Of Languages (jcl)
Self-discovery in Paulo Coelho’s Aleph
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There is no doubt that complexities and ambiguities exist in the human minds. The need to unravel and solve those mysteries and the foggy actualities of life’s existence has been one of Paulo Coelho’s (1947-present) biggest concern. In almost all his novels, Coelho shows how people should dig deep into the mysteries of life in order to find a true understanding to their existence. In doing so, a true kind of enlightenment and purification is reached and the quest for self-discovery or self-realization is attained.

    The paper tackles one of Paulo Coelho’s novels Aleph (2011) which is considered as an autobiographical reading to signify the connection between himself and God. It was a phase

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