24 research outputs found

    Arabic (Indian) Handwritten‏ ‏Digits Recognition Using Multi feature and KNN Classifier

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    تقدم هذه الورقة نظام التعرف على أرقام مكتوبة بخط اليد العربية على أساس الجمع بين أساليب الاستخراج متعددة المزايا، مثل   الملف الجانبي العلوي، ورأسية _ الإسقاط الأفقي وتحويل جيب التمام منفصلة مع  الانحراف المعياري.   يتم استخراج هذه الميزات من الصورة بعد تقسيمها الى عدة كتل.   المصنف KNN يستخدم لغرض التصنيف. يتم اختبار هذا العمل مع قاعدة بيانات ADBase القياسية (الأرقام العربية)، والتي تتكون من  70,000 أرقام  تم كتابتها من قبل 700 شخص مختلف.  في النظام المقترح يستخدم 60000  صورة رقم  لمرحلة التدريب و 10000 صورة رقم في مرحلة الاختبار. حقق هذا العمل دقة تعرف على  الارقام مقدارها  97.32٪.This paper presents an Arabic (Indian)  handwritten digit recognition system based on combining  multi feature  extraction methods, such a upper_lower  profile, Vertical _ Horizontal projection and Discrete Cosine Transform (DCT) with Standard Deviation σi called (DCT_SD)  methods. These  features are extracted from the image  after dividing it by several blocks. KNN classifier used  for classification purpose. This work is tested with the ADBase standard database (Arabic numerals),  which consist of 70,000 digits were 700 different writers write  it. In proposing system used 60000 digits, images for training phase and 10000 digits, images in testing phase. This work  achieved  97.32%  recognition  Accurac

    Gender detection in children’s speech utterances for human-robot interaction

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    The human voice speech essentially includes paralinguistic information used in many real-time applications. Detecting the children’s gender is considered a challenging task compared to the adult’s gender. In this study, a system for human-robot interaction (HRI) is proposed to detect the gender in children’s speech utterances without depending on the text. The robot's perception includes three phases: Feature’s extraction phase where four formants are measured at each glottal pulse and then a median is calculated across these measurements. After that, three types of features are measured which are formant average (AF), formant dispersion (DF), and formant position (PF). Feature’s standardization phase where the measured feature dimensions are standardized using the z-score method. The semantic understanding phase is where the children’s gender is detected accurately using the logistic regression classifier. At the same time, the action of the robot is specified via a speech response using the text to speech (TTS) technique. Experiments are conducted on the Carnegie Mellon University (CMU) Kids dataset to measure the suggested system’s performance. In the suggested system, the overall accuracy is 98%. The results show a relatively clear improvement in terms of accuracy of up to 13% compared to related works that utilized the CMU Kids dataset

    Modern drowsiness detection techniques: a review

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    According to recent statistics, drowsiness, rather than alcohol, is now responsible for one-quarter of all automobile accidents. As a result, many monitoring systems have been created to reduce and prevent such accidents. However, despite the huge amount of state-of-the-art drowsiness detection systems, it is not clear which one is the most appropriate. The following points will be discussed in this paper: Initial consideration should be given to the many sorts of existing supervised detecting techniques that are now in use and grouped into four types of categories (behavioral, physiological, automobile and hybrid), Second, the supervised machine learning classifiers that are used for drowsiness detection will be described, followed by a discussion of the advantages and disadvantages of each technique that has been evaluated, and lastly the recommendation of a new strategy for detecting drowsiness

    A Survey on Emotion Recognition for Human Robot Interaction

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    With the recent developments of technology and the advances in artificial intelligent and machine learning techniques, it becomes possible for the robot to acquire and show the emotions as a part of Human-Robot Interaction (HRI). An emotional robot can recognize the emotional states of humans so that it will be able to interact more naturally with its human counterpart in different environments. In this article, a survey on emotion recognition for HRI systems has been presented. The survey aims to achieve two objectives. Firstly, it aims to discuss the main challenges that face researchers when building emotional HRI systems. Secondly, it seeks to identify sensing channels that can be used to detect emotions and provides a literature review about recent researches published within each channel, along with the used methodologies and achieved results. Finally, some of the existing emotion recognition issues and recommendations for future works have been outlined

    Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer

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    In modern society, providing safe and collision-free travel is essential. Therefore, detecting the drowsiness state of the driver before its ability to drive is compromised. For this purpose, an automated hybrid sleepiness classification system that combines the artificial neural network and gray wolf optimizer is proposed to distinguish human Sleepiness and fatigue. The proposed system is tested on data collected from 15 drivers (male and female) in alert and sleep-deprived conditions where physiological signals are used as sleep markers. To evaluate the performance of the proposed algorithm, k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN) classifiers have been used. The results show that the proposed hybrid method provides 99.6% accuracy, while the SVM classifier provides 93.0% accuracy when the kernel is (RBF) and outlier (0.1). Furthermore, the k-NN classifier provides 96.7% accuracy, whereas the standalone ANN algorithm provides 97.7% accuracy

    Mobile Robot Path Planning Method Using Firefly Algorithm for 3D Sphere Dynamic & Partially Known Environment

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    اذا البحث يقترح طريقة لحل مشكلة تخطيط مسار الروبوت المتحرك في ضمن بيئة شبه معروفة ثلاثية الابعاد كروية الشكل باستخدام نسخة معدلة من خوارزمية الحشرات المضيئة Firefly Algorithm والتي تمكنت بنجاح من ايجاد طريق شبه مثالي خالي من التصادم مع العوائق بسرعة وسهولة وملاحة آمنة على طول الطريق حتى الوصول للهدف. In this paper, a new method is proposed to solve the problem of path planning for a mobile robot in a dynamic-partially knew three-dimensional sphere environment by using a modified version of the Firefly Algorithm that successfully finds near optimal and collision-free path while maintaining quick, easy and completely safe navigation throughout the path to the goal

    A survey on bio-signal analysis for human-robot interaction

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    The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of each domain's benefits and drawbacks, and finally, a recommendation for a new strategy for robotic systems

    Integrating Neural Network With Genetic Algorithms For The Classification Plant Disease

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    In this work Aِِrtificial Neural Network (ANN) is used as a classifier capable ofrecognizing the most important features of the plant disease, with minimum errorvalue. Genetic algorithm has been used to minimize error values of the ANNclassifier. Error value of ANN classifier is defined as more than (%5). This ratiois a threshold (cut-of-value) to determine if GA is executed or not after the ANNclassifier execution. Genetic algorithm execution results in either optimal solution(%100) recognition or suggests a modified parameter to the ANN classifier(specifically learning rate and number of neurons).The result obtained fromintegrating neural network with genetic algorithm for classification plant diseasesindicates that the classifier recognizes most of input pattern with accuracy (96%).Integrating neural network with genetic algorithm for classification plant diseasesimplemented using Visual Basic version 6 programmin

    Design and Implementation of Secure Public Key Steganography

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    Several approaches and techniques have been proposed to makecommunication via the internet secure; one of these approaches is steganography.Steganography is the art and science of hiding communication, a steganographicsystem thus embeds hidden content in unremarkable cover media so as not toarouse an eavesdropper’s suspicion. In this research the proposed method is apublic key steganography technique and it embeds a secret message in digitalimage with multi level of security. The proposed method uses Discrete CosineTransform (DCT) to embed a secret message in Bit Mapped Image Format forMicrosoft Windows (BMP) image with high invisibility and high correlationbetween cover-image and stego-image acceptable hiding data rate

    Writer Identification Based on Arabic Handwriting Recognition by using Speed Up Robust Feature and K- Nearest Neighbor Classification

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    In a writer recognition system, the system performs a “one-to-many” search in a large database with handwriting samples of known authors and returns a possible candidate list. This paper proposes method for writer identification handwritten Arabic word without segmentation to sub letters based on feature extraction speed up robust feature transform (SURF) and K nearest neighbor classification (KNN) to enhance the writer's  identification accuracy. After feature extraction, it can be cluster by K-means algorithm to standardize the number of features. The feature extraction and feature clustering called to gather Bag of Word (BOW); it converts arbitrary number of image feature to uniform length feature vector. The proposed method experimented using (IFN/ENIT) database. The recognition rate of experiment result is (96.666)
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