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نبذة عني

Dr. Ibrahim is an AI and software development expert with 25 years of experience…

مقالات Ibrahim

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النشاط

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الخبرة والتعليم

  • Valeo

عرض خبرة Ibrahim الكاملة

تعرّف على المسمى الوظيفي للأشخاص ومعدل بقائهم في العمل والكثير غير ذلك.

أو

بالنقر على الاستمرار للانضمام أو تسجيل الدخول، فأنت توافق على اتفاقية المستخدم واتفاقية الخصوصية وسياسة ملفات تعريف الارتباط على LinkedIn.

التراخيص والشهادات

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الخبرات التطوعية

المنشورات

  • Exploring applications of deep reinforcement learning for real-world autonomous driving systems

    VISAPP 2019: International Conference on Computer Vision Theory and Applications

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  • End-to-End Framework for Fast Learning Asynchronous Agents

    NeurIPS 2018: Imitation Learning and its Challenges in Robotics

    The ability to imitate by learning from demonstrations is used by robots to derive a policy. However, the quality of a learned policy depends mainly on the quality of the provided demonstrations. In Reinforcement Learning (RL), an agent learns optimal policy through interacting with its environment and receiving sparse reward signals, leading to a time consuming learning process. In this work, we aim to combine the benefits of imitation learning (IL) and deep RL. We propose a novel training…

    The ability to imitate by learning from demonstrations is used by robots to derive a policy. However, the quality of a learned policy depends mainly on the quality of the provided demonstrations. In Reinforcement Learning (RL), an agent learns optimal policy through interacting with its environment and receiving sparse reward signals, leading to a time consuming learning process. In this work, we aim to combine the benefits of imitation learning (IL) and deep RL. We propose a novel training framework for speeding up the training process through extending the Asynchronous Advantage Actor-Critic (A3C) algorithm by IL, leveraging multiple, non-human imperfect mentors. ViZDoom, a 3D world software, is used as a test case. The experimental results show that the learning agent achieves a better performance than the mentors. Furthermore, in comparison to the standard A3C algorithm, the proposed training framework succeeds to attain the same performance, while achieving 2.7X faster learning.

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  • End-To-End Multi-Modal Sensors Fusion System For Urban Automated Driving

    NeurIPS 2018: Machine Learning for Intelligent Transportation Systems

    Abstract: In this paper, we present a novel framework for urban automated driving based on multi-modal sensors; LiDAR and Camera. Environment perception through sensors fusion is key to successful deployment of automated driving systems, especially in complex urban areas. Our hypothesis is that a well designed deep neural network is able to end-to-end learn a driving policy that fuses LiDAR and Camera sensory input, achieving the best out of both. In order to improve the generalization and…

    Abstract: In this paper, we present a novel framework for urban automated driving based on multi-modal sensors; LiDAR and Camera. Environment perception through sensors fusion is key to successful deployment of automated driving systems, especially in complex urban areas. Our hypothesis is that a well designed deep neural network is able to end-to-end learn a driving policy that fuses LiDAR and Camera sensory input, achieving the best out of both. In order to improve the generalization and robustness of the learned policy, semantic segmentation on camera is applied, in addition to applying our new LiDAR post processing method; Polar Grid Mapping (PGM). The system is evaluated on the recently released urban car simulator, CARLA. The evaluation is measured according to the generalization performance from one environment to another. The experimental results show that the best performance is achieved by fusing the PGM and semantic segmentation.
    Keywords: End-to-end learning, Conditional imitation learning, Sensors fusion

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  • YOLO4D: A Spatio-temporal Approach for Real-time Multi-object Detection and Classification from LiDAR Point Clouds

    NIPS

    Abstract: In this paper, YOLO4D is presented for Spatio-temporal Real-time 3D Multi-object detection and classification from LiDAR point clouds. Automated Driving dynamic scenarios are rich in temporal information. Most of the current 3D Object Detection approaches are focused on processing the spatial sensory features, either in 2D or 3D spaces, while the temporal factor is not fully exploited yet, especially from 3D LiDAR point clouds. In YOLO4D approach, the 3D LiDAR point clouds are…

    Abstract: In this paper, YOLO4D is presented for Spatio-temporal Real-time 3D Multi-object detection and classification from LiDAR point clouds. Automated Driving dynamic scenarios are rich in temporal information. Most of the current 3D Object Detection approaches are focused on processing the spatial sensory features, either in 2D or 3D spaces, while the temporal factor is not fully exploited yet, especially from 3D LiDAR point clouds. In YOLO4D approach, the 3D LiDAR point clouds are aggregated over time as a 4D tensor; 3D space dimensions in addition to the time dimension, which is fed to a one-shot fully convolutional detector, based on YOLO v2. The outputs are the oriented 3D Object Bounding Box information, together with the object class. Two different techniques are evaluated to incorporate the temporal dimension; recurrence and frame stacking. The experiments conducted on KITTI dataset, show the advantages of incorporating the temporal dimension.
    Keywords: 3D object detection, LiDAR, Real-time, Spatiotemporal, ConvLSTM

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  • Robust Dual View Deep Agent

    Proceeding of the 2nd International Sino-Egyptian Congress on Agriculture, Veterinary Sciences and Engineering, 2017

    Motivated by recent advance of machine learning using Deep Reinforcement Learning this paper proposes a modified architecture that produces more robust agents and speeds up the training process. Our architecture is based on Asynchronous Advantage Actor-Critic (A3C) algorithm where the total input dimensionality is halved by dividing the input into two independent streams. We use ViZDoom, 3D world software that is based on the classical first person shooter video game, Doom as a test case. The…

    Motivated by recent advance of machine learning using Deep Reinforcement Learning this paper proposes a modified architecture that produces more robust agents and speeds up the training process. Our architecture is based on Asynchronous Advantage Actor-Critic (A3C) algorithm where the total input dimensionality is halved by dividing the input into two independent streams. We use ViZDoom, 3D world software that is based on the classical first person shooter video game, Doom as a test case. The experiments show that in comparison to single input agents, the proposed architecture succeeds to have the same playing performance and shows more robust behavior, achieving significant reduction in the number of training parameters of almost 30%.

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  • Statistical Formant Speech Synthesis for Arabic

    Springer, Arabian Journal for Science and Engineering

    This work constructs a hybrid system that integrates formant synthesis and context-dependent Hidden Semi-Markov Models (HSMM). HSMM parameters comprise of formants, fundamental frequency, voicing/frication amplitude, and duration. For HSMM training, formants, fundamental frequency, and voicing/frication amplitude are extracted from waveforms using the Snack toolbox and a decomposition algorithm, and duration is calculated using HMM modeled by multivariate Gaussian distribution. The acoustic…

    This work constructs a hybrid system that integrates formant synthesis and context-dependent Hidden Semi-Markov Models (HSMM). HSMM parameters comprise of formants, fundamental frequency, voicing/frication amplitude, and duration. For HSMM training, formants, fundamental frequency, and voicing/frication amplitude are extracted from waveforms using the Snack toolbox and a decomposition algorithm, and duration is calculated using HMM modeled by multivariate Gaussian distribution. The acoustic features are then generated from the trained HSMM models and combined with default values of complementary acoustic features such as glottal waveform parameters to produce speech waveforms utilizing the Klatt synthesizer. We construct the text processor for phonetic transcription required at the training and synthesis phases by utilizing phonemic pronunciation algorithms. A perceptual test reveals that the statistical formant speech text-to-speech system produces good-quality speech while utilizing features that are small in dimension and close to speech perception cues.

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  • Building an Arabic Lexical Semantic Analyzer

    7th International Computing Conference in Arabic, Riyadh Saudi Arabia

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  • Evaluation Approaches for an Arabic Extractive Generic Text Summarization System

    2nd International Conference on Arabic Language Resources and Tools, Cairo Egypt

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  • An Optimized Dual Classification System for Arabic Extractive Generic Text Summarization

    The Seventh Conference on Language Engineering, ECLEC, Cairo Egypt

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  • A Trainable Arabic Bayesian Extractive Generic Text Summarizer

    The Sixth Conference on Language Engineering, ECLEC, Cairo Egypt

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الدورات التعليمية

  • An Introduction to Database Systems, 8th Edition, C.J. Date

    -

  • Browser-based Models with TensorFlow.js by deeplearning.ai and Google Brain

    F56469GL5HL2

  • Computer Networks, 4th Edition, Andrew S. Tanenbaum

    -

  • Configuration Management: QAI

    -

  • Cryptography and Network Security, 4th Edition, William Stallings

    -

  • Deep Learning

    Oxford - 2015

  • Essentials of Rational® RequisitePro® IBM®, Rational® Software

    -

  • Essentials of Requirement Management: Quality Assurance Institute (QAI)

    -

  • Hidden Markov Models- HMM, A tutorial on hidden Markov models and selected applications, Rabiner

    -

  • Human Resource Management

    -

  • Image Processing, Analysis, and Machine Vision, 3rd Edition, Sonka

    -

  • Integrated Business Skills Training (Amideast)

    -

  • Introduction to Big Data with Apache Spark

    UC, Berkeleyx - cs100

  • Machine Learning

    Stanford University

  • Machine Learning and Data Mining

    UBC - CPSC 340

  • Marketing Essentials

    -

  • Mastering Requirements IBM®, Rational® Software Management with "Use Cases"

    -

  • PMP Exam Preparation

    -

  • Partially Observed Markov Decision Process POMDP, KALMAN Filters, Game Theory: Artificial Intelligence, A modern Approach, 2nd Edition

    -

  • Proposal Writing

    -

  • Scalable Machine Learning

    UC, Berkeleyx - cs190

  • Social Networks, Crawling the Web: Discovering Knowledge from Hypertext Data, Chakrabarti. • Probability and Markov Process

    -

  • Software Estimation: QAI

    -

  • Structured Methods for Software Testing: QAI

    -

التكريمات والمكافآت

  • Received "A" Grade for the year 2022

    Valeo

  • Inventor 2016-2018 (Medal)

    Valeo

    #Deeplearning #Technology #Automotive #innovation

  • Senior Expert of AI

    Valeo

    Machine Learning and Deep Learning

  • Spark ML

    OMS Research Department

    Successfully completing the course: Introduction to Big Data with Apache Spark
    Learn how to apply data science techniques using parallel programming in Apache Spark to explore big (and small) data.
    https://www.edx.org/course/introduction-big-data-apache-spark-uc-berkeleyx-cs100-1x#!

  • Excellent Performance and Commitment

    RDI www.rdi-eg.com

  • Self learning of pronunciation rules, Speech Recognition Technology, Hafss©

    WORLD SUMMIT AWARDS: www.wsis-award.org

  • Microsoft Middle East Developer Conference (MDC), a winner of .NET competition

    Microsoft

  • Best Performance, Al Bayan Educational Project (E-Learning)

    RDI www.rdi-eg.com

  • General Best Performance Rate

    RDI www.rdi-eg.com

  • Best Creativity and inventiveness

    RDI www.rdi-eg.com

  • Best Performance, Applying ISO 9001 in Application Development Unit

    RDI www.rdi-eg.com

  • A winner of game programming competition

    Cairo University, Faculty of Engineering, Computer Science Society (CSS)

نتائج الاختبارات

  • TOEFL

    النتيجة: 607

التوصيات المستلمة

15شخصا قدموا توصية لـIbrahim

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المزيد من أنشطة Ibrahim

عرض ملف Ibrahim الشخصي الكامل

  • مشاهدة الأشخاص المشتركين الذين تعرفهم
  • تقديم تعارف
  • تواصل مع Ibrahim مباشرة
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ملفات شخصية أخرى مشابهة

استكشاف مزيد من المنشورات

أعضاء آخرون يحملون اسم ⁦⁩Ibrahim Sobh - PhD⁦⁩ في ⁦⁩مصر

13 عضو آخر يحملون اسم Ibrahim Sobh - PhD في مصر على LinkedIn

استعرض الأعضاء الآخرين الذين يحملون اسم ⁦⁩Ibrahim Sobh - PhD