الصفحة 1
الصفحة 1
IOT control and surveillance system
An automated system is a combination of both software and hardware which is designed and programmed to work automatically without the need of any human operator to provide inputs and instructions for each operation. The Internet of Things (IoT) is a network of connected things. These ‘things’ (devices) communicate with each other using machine to machine communication (M2M). Information is traversed between devices so that processes can be automated, without the need for human intervention. By reducing the number of people involved in a business process, several advantages arise, including improved accuracy and up-time. We will build an IoT automated system to control access of humans and vehicles to a warehouse based on biometrics and image recognition techniques.
Image recognition : progress, trends and challenges
Focuses on research trends in image processing and recognition and corresponding developments. Among them, the book focuses on recent research, especially in the field of advanced human-computer interaction and intelligent computing. Given the existing interaction and recognition of the station, some novel topics are proposed, including how to establish a cognitive model in human-computer interaction and how to express and transfer human knowledge into human-machine image recognition. In an interactive implementation, how to implement user experience through image recognition during machine interaction
Food lens = فود لينس
Food lens is an innovative application designed to revolutionize dietary management by leveraging advanced image recognition and nutritional analysis. The primary objective of this senior project is to develop a user-friendly tool that identifies various foods through a camera interface and provides detailed nutritional information. This application not only enhances the user's understanding of their dietary intake but also assists in achieving personalized health and fitness goals. The core functionality of Food Lens involves the integration of a robust image recognition system capable of accurately identifying a wide range of foods. Upon identification, the application retrieves comprehensive nutritional data, including calorie content, macronutrient distribution (proteins, fats, carbohydrates), and essential micronutrients (vitamins and minerals). This data is then seamlessly integrated into the user's dietary profile. Food Lens is designed to track the user's daily caloric intake and compare it against personalized recommendations based on age, gender, weight, height, and activity level. By maintaining a dynamic record of consumed foods, the application provides real-time feedback on the user’s nutritional progress. This feature is particularly beneficial for individuals aiming to manage weight, address dietary restrictions, or improve overall health.
Explainable AI with Python
This book provides a full presentation of the current concepts and available techniques to make “machine learning” systems more explainable. The approaches presented can be applied to almost all the current “machine learning” models: linear and logistic regression, deep learning neural networks, natural language processing and image recognition, among the others.
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).
Machine learning challenges : Evaluating predictive uncertainty, Visual Object Classification, and Recognizing Textual Entailment, 1st Pascal Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers
Constitutes the refereed post-proceedings of the First PASCAL Machine Learning Challenges Workshop, MLCW 2005. 25 papers address three challenges: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; second, recognizing objects from a number of visual object classes in realistic scenes; third, recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.
Big Data : Conceptual Analysis and Applications
The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used.
Arabic AI-generated adverts = إعلانات عربية مولدة بالذكاء الاصطناعي
In the ever-evolving landscape of digital marketing, our groundbreaking platform emerges, harnessing the power of artificial intelligence to revolutionize the creation of marketing campaigns. This innovative solution seamlessly combines neural networks, natural language generation, and multimedia processing capabilities to generate comprehensive and personalized marketing assets from a single product image. At its core, the platform leverages deep learning models that analyze the visual characteristics of the uploaded product image and identifies key features and attributes to define the product category, this enables the generation of compelling and persuasive marketing content tailored to the specific product. The platform's multi-faceted approach encompasses three distinct components: text generation, poster creation, and video advertisement production. Utilizing advanced natural language generation techniques, it crafts engaging marketing text that captures the essence and unique selling points of the product. Simultaneously, its image processing capabilities produce visually stunning promotional posters that blend the product image with eye-catching graphics and typography. along with creating captivating video advertisements optimized for social media and online advertising campaigns.
