الصفحة 2
الصفحة 2
Computational science -- ICCS 2005 ; Vol. 3514 ; 5th International Conference, Atlanta, GA, USA, May 22-25, 2005, Proceedings, Part I
This book constitutes the refereed proceedings of the Fifth International Conference on Computational Science (ICCS 2005) held in Atlanta, Georgia, USA, 2005, Computational science is rapidly maturing as a mainstream discipline. It is central to an ever-expanding variety of ?elds in which computational methods and tools enable new discoveries with greater accuracy and speed. The primary objectives of this conference were to discuss problems and solutions in allareas,toidentifynewissues,toshapefuturedirectionsofresearch,andtohelp users apply various advanced computational techniques. The event highlighted recent developments in algorithms, computational kernels, next generation c- puting systems, tools, advanced numerical methods, data-driven systems, and emerging application ?elds, such as complex systems, ?nance, bioinformatics, computational aspects of wireless and mobile networks, graphics, and hybrid computation.
Matching Properties of Deep Sub-Micron MOS Transistors
Matching Properties of Deep Sub-Micron MOS Transistors examines this interesting phenomenon. Microscopic fluctuations cause stochastic parameter fluctuations that affect the accuracy of the MOSFET. For analog circuits this determines the trade-off between speed, power, accuracy and yield.
Classification and Modeling with Linguistic Information Granules : Advanced Approaches to Linguistic Data Mining
Many approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod els. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe matical models even when they are simple and linear. This is because human information processing is based mainly on linguistic knowledge while com puter systems are designed to handle symbolic and numerical information. A large part of our daily communication is based on words. We learn from various media such as books, newspapers, magazines, TV, and the Inter net through words. We also communicate with others through words. While words play a central role in human information processing, linguistic models are not often used in the fields of classification and modeling. If there is no goal other than the maximization of accuracy in classification and modeling, mathematical models may always be preferred to linguistic models. On the other hand, linguistic models may be chosen if emphasis is placed on interpretability.
Car deal : The ultimate used-cars marketplace
This is an effort to represents the design and implementation of a mobile application that serves as a marketplace for buying and selling used cars. The application is developed using Flutter, a popular cross-platform framework, and integrates an Artificial Intelligence (AI) model to predict the price of used cars based on various parameters, such as the car's model, age, mileage, and condition. The report provides a comprehensive overview of the project's development process, including the use of agile methodology and various technologies, such as Firebase, Python, and TensorFlow. The AI model's accuracy is evaluated using statistical metrics, such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
Big Data in Context : Legal, Social and Technological Insights
Sheds new light on a selection of big data scenarios from an interdisciplinary perspective. It features legal, sociological and economic approaches to fundamental big data topics such as privacy, data quality and the ECJ’s Safe Harbor decision on the one hand, and practical applications such as smart cars, wearables and web tracking on the other. Addressing the interests of researchers and practitioners alike, it provides a comprehensive overview of and introduction to the emerging challenges regarding big data.All contributions are based on papers submitted in connection with ABIDA (Assessing Big Data), an interdisciplinary research project exploring the societal aspects of big data and funded by the German Federal Ministry of Education and Research.
Battery management systems : Accurate state-of-charge indication for battery-powered applications
Builds further on the contents of the first volume in the Philips Research Book Series, Battery Management Systems - Design by Modelling. Since the subject of battery SoC indication requires a number of disciplines, this book covers all important disciplines starting from (electro)chemistry to understand battery behaviour, via mathematics to enable modelling of the observed battery behaviour and measurement science to enable accurate measurement of battery variables and assessment of the overall accuracy, to electrical engineering to enable an efficient implementation of the developed SoC indication system. It will therefore serve as an important source of information for any person working in engineering and involved in battery management.
Automatic video editor
Searching in a large database of videos is one of the challenges faced by the user today as most of the results are inaccurate or correct. In our project, we worked on developing a system that receives the search word from the user and searches for it among a large number of videos using MSR-VTT dataset and COCO data set based on the elements that we see inside the video. Entered by the user. We have also worked on adding other options that the user can benefit from in modifying the videos, such as entering a black and white video clip and returning the result in color. The user can also enter a low-resolution video clip, and the system improves the accuracy of the video and sends it.
Algorithms and data structures for massive datasets
Learn: Probabilistic sketching data structures for practical problems Choosing the right database engine for your application Evaluating and designing efficient on-disk data structures and algorithms Understanding the algorithmic trade-offs involved in massive-scale systems Deriving basic statistics from streaming data Correctly sampling streaming data Computing percentiles with limited space resources Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You'll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects--and there's no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you'll find the sweet spot of saving space without sacrificing your data's accuracy. About the Technology Standard algorithms and data structures may become slow--or fail altogether--when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost.
Algorithmic Learning in a Random World
This new monograph integrates mathematical theory and revealing experimental work. It demonstrates mathematically the validity of the reliability claimed by conformal predictors when they are applied to independent and identically distributed data, and it confirms experimentally that the accuracy is sufficient for many practical problems. Later chapters generalize these results to models called repetitive structures, which originate in the algorithmic theory of randomness and statistical physics. The approach is flexible enough to incorporate most existing methods of machine learning, including newer methods such as boosting and support vector machines and older methods such as nearest neighbors and the bootstrap.
AI voice dubbing = الدبلجة الصوتية بالذكاء الاصطناعي
The advent of artificial intelligence has revolutionized numerous domains, including media and entertainment. This project focuses on developing an AI Voice Dubbing application that facilitates the seamless dubbing of audio content from English to Arabic. Unlike traditional robotic voice outputs, our application ensures that the dubbed content retains the original voice characteristics, providing a natural and authentic listening experience. The system also offers users the option to select alternative output voices, including those of celebrities, or any other voice by uploading their media, providing a YouTube link, or recording audio directly. The core functionality of the application is to produce a dubbed audio file, maintaining high fidelity to the original speaker's voice tone and style, the application provides a text output of the dubbed content, which users can edit as needed to ensure accuracy and personal preference.
Advances in visual computing ; Vol. 4292 ; 2nd International symposium, ISVC 2006, Lake Tahoe, NV, USA, November 6-8, 2006, Proceedings, Part II
This year, the program consisted of 13 oral sessions, one poster session, ten special tracks, and six keynote presentations. The response to the call for - pers was very strong. We received more than twice the papers received last year. Specifcally, we received over 280 submissions for the main symposium from which we accepted 65 papers for oral presentation (23% acceptance) and 56 papers for poster presentation (20% acceptance). Special track papers were solicited separately through the Organizing and Program Committees of each track. A total of 57 papers were accepted for presentation in the special tracks. All papers were reviewed with an emphasis on potential to contribute to the state of the art in the ?eld. Selection criteria included accuracy and originality of ideas, clarity and signi?cance of results, and presentation quality. The review process was quite rigorous, involving two to three independent blind reviews followed by several days of discussion.
Advances in Visual Computing ; Vol. 4291 ; 2nd International Symposium, ISVC 2006, Lake Tahoe, NV, USA, November 6-8, 2006, Proceedings, Part I
This year, the program consisted of 13 oral sessions, one poster session, ten special tracks, and six keynote presentations. The response to the call for - pers was very strong. We received more than twice the papers received last year. Specifcally, we received over 280 submissions for the main symposium from which we accepted 65 papers for oral presentation (23% acceptance) and 56 papers for poster presentation (20% acceptance). Special track papers were solicited separately through the Organizing and Program Committees of each track. A total of 57 papers were accepted for presentation in the special tracks. All papers were reviewed with an emphasis on potential to contribute to the state of the art in the ?eld. Selection criteria included accuracy and originality of ideas, clarity and signi?cance of results, and presentation quality. The review process was quite rigorous, involving two to three independent blind reviews followed by several days of discussion. During the discussion period we tried to correct anomalies and errors that might have existed in the initial reviews.
Advances in Case-Based Reasoning ; 9th European Conference, ECCBR 2008 , Trier, Germany, September 1-4, 2008. Proceedings
Constitutes the refereed proceedings of the 9th European Conference on Case-Based Reasoning, ECCBR 2008, held in Trier, Germany, in September 2008.
3D Segmentation for medical images (OsteoVision) = التقطيع ثلاثي الأبعاد للصور الطبية
With the increasing integration of AI across various sectors, artificial intelligence (AI) is already playing a significant role in the healthcare industry, and its use is expected to grow further. AI systems used in image processing and computer vision algorithms have shown a significant ability to perform many operations such as segmentation, classification, and detection. This project presents the application of computer vision algorithms in the field of medical imaging for diagnostic, therapeutic, and interventional purposes. This thesis explores the use of several computer vision algorithms to address different pathologies, specifically brain tumors (glioma) (see Appendix A) and knee osteoarthritis (OA), as well as tracking the progression of knee osteoarthritis using the Kellgren and Lawrence (KL) grading system, a common method for classifying the severity of OA into five grades. To achieve the desired impact, the project employs various techniques, including 3D segmentation for brain tumors, 2D segmentation for knee joints, and multinomial classification for determining the severity of knee OA injuries. The primary aims of the project are to enhance diagnostic accuracy, assist in creating treatment plans, provide an assistive tool for healthcare providers to make more informed decisions, leverage AI's capabilities to detect abnormalities that might escape the human eye, and streamline workflow. To facilitate these goals, the project incorporates a user-friendly UI, a website, and a Flutter-based mobile application, enabling healthcare providers to efficiently integrate these tools into their practice and improve patient care.
