الصفحة 3
الصفحة 3
English proofreader
Using Natural Language Processing via Deep Learning, we will attempt to implement a context-sensitive spelling error correction system focused on casual text messaging. For example, usual autocorrect systems will not correct a sentence like “An apple is better than a banana” as this is a syntactically correct sentence. However, a correction should be made here to the word “then” and the sentence should be, “An apple is better than a banana.” These types of mistakes are common in people texting and can be found for example in people learning English as a second language. Natural Language Processing can be summarized as combining the utilization of computation to understand the concepts of natural language and speech.
Embedded robotics : From mobile robots to autonomous vehicles with Raspberry Pi and Arduino
Presents a unique examination of mobile robots and autonomous vehicles using embedded systems, from introductory to advanced level. It is structured in four parts, dealing with Embedded Systems (processors, sensors, actuators, control, multitasking and communication), Robot Hardware (driving and walking robots, autonomous boats and planes, as well as robot manipulators), Robot Software (localization, navigation, image processing and automotive systems), and Artificial Intelligence (neural networks, genetic algorithms and deep learning). The book is organized for ease of use, with numerous figures, photographs, and worked example programs. The book is written as a text for courses in computer science, computer engineering, IT, electronics engineering, and mechatronics, as well as a guide for robot hobbyists and researchers.
Elevating video content creation with ai assistance = ارتقاء إنشاء محتوى الفيديو بمساعدة الذكاء الاصطناعي
We developed an AI Assistant equipped with features such as description crafting, title generation, keyword extraction, image captioning, clickbait detection, and sentiment analysis.To achieve these functionalities, we proposed a model for generating video descriptions using ResNet50 as a feature extractor and a LSTM network with an attention mechanism as a sequence generator, achieving a BLEU-1 score of 0.907 and a ROUGE-L score of 0.645. For keyword extraction, we utilized Sentence Transformer to identify strategically relevant keywords from the generated descriptions. For title generation, we fine-tuned the BART model, achieving a ROUGE-L score of 0.45. For clickbait detection, we used SVC classifier with linear kernel and TF-IDF vectorization for feature extraction, resulting in 96% accuracy. Our sentiment analysis model using a CNN-LSTM architecture achieved 80% accuracy in analyzing comments on videos. For image captioning, we employed a feature extractor with a CNN layer followed by an LSTM model, achieving a BLEU-1 score of 0.53. Our platform empowers creators by simplifying complex tasks and offering deeper audience engagement insights, making it a powerful tool in the evolving digital content creation.
Drousi: A private lessons management system
Private teaching is considered to be an effective way to increase academic achievement of students and it seen as being a supplementary education, so it has become very common all around the world, however, previous studies have come to different conclusions regarding its necessity, so Drousi application had been made ,it’s a user-friendly mobile application to manage and arrange private lessons, and There has never been a better time to become a private tutor, as class sizes in schools increase, this can result in teachers being unable to give their undivided attention to students, and this document lays out a project plan for the development of “Drousi”, The plan will include a summary of the system functionality and how it implemented.
Dr.phone
Dr phone is a software system that helps in talking with the doctor automatically and easily without the need to go to the doctor's clinic to diagnose the patient's condition. our application presents an available platform to make a video call between the doctor and the patient according to the patient’s needs. The system accepts the patient’s request after choosing an available doctor andthen waits for the doctor to accept his request, if there is no doctor available, the system performs an AI chatbot to the patient's need to give him the appropriate diagnosis. when the call finished the doctor represents medical record including the medicine and the analytics and record the next appointment if it’s needed then send them to the patient's email, the patient also can see the nearest pharmacies or labs according to his location, and finally the patient rates the doctor after the call is finished then payment by his available wallet.
Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety
Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.
Deep Learning-Based Face Analytics
Provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolving field.
Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics : Techniques and Applications
Examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever.
Deep Learning with PyTorch Lightning : Build and train high-performance artificial intelligence and self-supervised models using Python
You’ll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. You’ll build a neural network architecture, deploy an application from scratch, and see how you can expand it based on your specific needs, beyond what the framework can provide. In the later chapters, you’ll also learn how to implement capabilities to build and train various models like Convolutional Neural Nets (CNN), Natural Language Processing (NLP), Time Series, Self-Supervised Learning, Semi-Supervised Learning, Generative Adversarial Network (GAN) using PyTorch Lightning.
Deep Learning to See : Towards New Foundations of Computer Vision
Topics and features: Presents a curiosity-driven approach, posing questions to stimulate readers to design novel computational models of vision Offers a rethinking of computer vision, arguing for an approach based on vision in nature, versus regarding visual signals as collections of images Provides an interdisciplinary commentary, aiming to unify computer vision, machine learning, human vision, and computational neuroscience Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions.
Deep learning pipeline : Building a deep learning model with TensorFlow
Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets.
Deep learning methods for converting speech to text = تقنيات التعلم العميق في تحويل الصوت إلى نص
Aims to design and develop a system capable of extracting audio content from films and audio recordings and converting it into text using deep learning techniques. This is done by analyzing audio patterns, extracting sounds and words from the video, and then converting them into written text. Deep learning, a branch of artificial intelligence, is used to accomplish this task. The study also includes comparing different deep learning techniques to determine their effectiveness in this context.
Deep learning for computational problems in hardware security : Modeling attacks on strong physically unclonable function circuits
Discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security.
Deep learning architecture and application
As one of the fastest-growing topics in machine learning, deep learning algorithms have achieved unprecedented success in recent years. Novel paradigms (such as contrastive learning and few-shot learning) in deep learning and rising neural network architectures (e.g., transformer and masked autoencoder) are dramatically changing the field of data-driven algorithms. More importantly, deep learning models are redefining the next generation of industrial applications spanning image recognition, speech processing, language translation, healthcare, and other sciences. For example, recent advances in deep representation learning are allowing us to learn about protein 3D structures, which sheds new light on fundamental medicine and biology along with potentially bringing in billions of dollars (e.g., in the pharmaceutical market).
Deep learning approaches to cloud security
Covering one of the most important subjects to our society today, cloud security, this editorial team delves into solutions taken from evolving deep learning approaches, solutions allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts.
Deep learning approach for text summarization
Machine learning and deep learning, as we know, have started ruling over almost every field in the computing industry and so, has revolutionized the process of text summarization too. Automatic text summarization is an advancing realm of the natural language processing research in which concise textual summaries are generated from lengthy input documents. Extensive research has been carried out on how automatic summarization can be prosecuted through various extractive and abstractive techniques. In this paper, we address all the approaches to text summarization and present the modus operandi of an Architecture called Encoder Decoder, under the machine learning approach.
Deep Learning and its Applications
Presents an introduction to deep learning and various applications of deep learning such as recommendation systems, text recognition, diabetic retinopathy prediction of breast cancer, prediction of epilepsy, sentiment, fake news detection, software defect prediction and protein function prediction.
Deep learning and computer vision in remote sensing-II
Computer vision (CV) have seen a massive rise in popularity in the remote sensing field over the last few years. This success is mostly due to the effectiveness of deep learning (DL) algorithms. However, remote sensing data acquisition and annotation, as well as information extraction from massive remote sensing data, are still challenging. This reprint collected novel developments in the field of deep learning and computer vision methods for remote sensing. Papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems, have been published. With practical examples and real-world case studies, this reprint provides a valuable resource for researchers, professionals, and students seeking to harness the power of deep learning in the field of remote sensing.
Deep learning and computer vision in remote sensing-I
In the last few years, huge amounts of progress have been made regarding remote sensing in the field of computer vision. This success and progress is mostly due to the effectiveness of deep learning (DL) algorithms. In addition, the remote sensing community has shifted its attention to DL, and DL algorithms have been used to achieve significant success in many image analysis tasks. However, with regard to remote sensing, a number of challenges caused by difficulties in data acquisition and annotation have not been fully solved yet. This reprint is a collection of novel developments in the field of remote sensing using computer vision, deep learning, and artificial intelligence. The articles published involve fundamental theoretical analyses as well as those demonstrating their application to real-world problems.
Deep fake detection
Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recently emerged is “deepfake”. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. The proposal of technologies that can automatically detect and assess the integrity of digital visual media is therefore indispensable.
