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Fiber-Optic Communication Systems
Contains substantial additions covering the topics of coherence detection, space division multiplexing, and more advanced subjects. You'll learn about topics like fiber’s losses, dispersion, and nonlinearities, as well as coherent lightwave systems. The latter subject has undergone major changes due to the extensive development of digital coherent systems over the last decade. Space-division multiplexing is covered as well, including multimode and multicore fibers developed in just the last ten years.
E-Voting and Identity ; 1st International Conference, VOTE-ID 2007, Bochum, Germany, October 4-5, 2007, Revised Selected Papers
Voting and identity have a very delicate relationship. Only a few processes - pendso much on an identity management respecting the fine line between reliable identification and reliable non-identifiability each at its part during the process. And only a few processes may change their outer appearance so much with the advent of new IT as voting and identity management do. So it was no surprise in FIDIS, the interdisciplinary Network of Excellence working on the Future of Identity in the Information Society
Evolutionary Synthesis of Pattern Recognition Systems
Evolutionary Synthesis of Pattern Recognition Systems presents novel effective approaches based on evolutionary computational techniques, such as genetic programming (GP), linear genetic programming (LGP), coevolutionary genetic programming (CGP) and genetic algorithms (GA) to automate the synthesis and analysis of object detection and recognition systems. The book’s concepts, principles, and methodologies will enable readers to automatically build robust and flexible systems—in a systematic manner—that can provide human-competitive performance and reduce the cost of designing and maintaining these systems. Its content covers all key aspects of object recognition: object detection, feature selection, feature discovery, object recognition, domain knowledge. Basic knowledge of programming and data structures, and some calculus, is presupposed.ing the book’s novel ideas
Error-Correction Coding and Decoding : Bounds, Codes, Decoders, Analysis and Applications
This book discusses both the theory and practical applications of self-correcting data, commonly known as error-correcting codes. The applications included demonstrate the importance of these codes in a wide range of everyday technologies, from smartphones to secure communications and transactions. Written in a readily understandable style,This book is a valuable resource for anyone interested in error-correcting codes and their applications, ranging from non-experts to professionals at the forefront of research in their field.
Emerging Trends in Information and Communication Security ; International Conference, ETRICS 2006, Freiburg, Germany, June 6-9, 2006. Proceedings
This book constitutes the refereed proceedings of the International Conference on Emerging Trends in Information and Communication Security, ETRICS 2006, held in Freiburg, Germany, in June 2006. The book presents 36 revised full papers, organized in topical sections on multilateral security; security in service-oriented computing, secure mobile applications; enterprise privacy; privacy, identity, and anonymity; security engineering; security policies; security protocols; intrusion detection; and cryptographic security.
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.
Egocentric video summarization
Video summarization is defined as the generation of a summary of extensive video content that comes from all kinds of videos including egocentric videos by detecting and presenting the material to potential users which is most informative and contains interesting information. Video summarization has many practical applications and Egocentric video summarization approaches have been proposed to solve various problems in the healthcare industry. This work focuses on Alzheimer. Patients suffering from Alzheimer’s face difficulties in remembering what happened during their day, the identity of persons and medicine they took.
Distributed computing and internet technology Vol. 4317 ; 3rd International conference, ICDCIT 2006, Bhubaneswar, India, December 20-23, 2006
Constitutes the refereed proceedings of the Third International Conference on Distributed Computing and Internet Technology, ICDCIT 2006, held in Bhubaneswar, India in December 2006. This book features the papers addressing and covering the areas distributed computing, internet technology, system security, data mining, and software engineering
Digital Mammography ; 8th International Workshop, IWDM 2006, Manchester, UK, June 18-21, 2006, Proceedings
This volume of Springer’s Lecture Notes in Computer Science series records th the proceedings of the 8 International Workshop on Digital Mammography (IWDM), which was held in Manchester, UK, June 18–21, 2006. The meetings bringtogetheradiversesetofresearchers(physicists,mathematicians,computer scientists, engineers), clinicians (radiologists, surgeons) and representatives of industry, who are jointly committed to developing technology, not just for its ownsake,but to supportclinicians inthe earlydetection andsubsequentpatient management of breast cancer.
Diabetes genetic finder & predictor = أداة البحث والتنبؤ الجيني لمرض السكري
The diabetes genetic finder & predictor app is a comprehensive, user-friendly solution that revolutionizes diabetes care. This powerful app integrates a wide array of features designed to empower diabetes patients and enhance their overall well-being. A standout feature of the app is its ability to predict the risk of hereditary diabetes diseases, offering users early detection and intervention opportunities. It also predicts general diabetes risk, diabetic foot complications, and retinopathy. Users can monitor their blood sugar levels, heart rate, and oxygenation either manually or through smart watch integration. Additionally, users can enter their diabetes type and HbA1c levels.The app's medication management feature simplifies the complex task of tracking and organizing medications, providing timely reminders to ensure adherence to treatment plans. Users can scan QR codes on products to check their sugar content and suitability, schedule their medications, generate reports for specific periods, and access a comprehensive list of frequently asked questions about diabetes..
Developing bus management system for AIU
To solve the problem of congestion in bus stops for students, members of the administrative and educational people at the Arab International University, an application was designed that allows the user to reserve a seat on the bus. The application provides prior reservation and enters the study time for the user, the application reminds him for the time of his going to the university. The basic functions of the application are designed according to the general analysis, The development of the application used Laravel, flutter frameworks, AI and MySQL database processing technology. The application has accomplished such functions as notification for location. The test of the application is running in good conditions. The use of the application will solve the problem of bus crowding. The efficiency of the platform makes it a very good candidate to be implemented for any person in Arab International University.
Detection of intrusions and malware, and vulnerability assessment ; 5th International Conference, DIMVA 2008, Paris, France, July 10-11, 2008. Proceedings
This book constitutes the refereed proceedings of the 5th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2008, held in Paris, France in July 2008.
Detection of intrusions and malware, and vulnerability assessment ; 4th International Conference, DIMVA 2007 Lucerne, Switzerland, July 12-13, 2007 Proceedings
This book presented malware; network security, Web security; attacks; Security-Intrusion; Detection and Response
Detection of intrusions and malware, and vulnerability assessment ; 3rd International Conference, DIMVA 2006, Berlin, Germany, July 13-14, 2006, Proceedings
This book constitutes the refereed proceedings of the Third International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2006, held in Berlin, Germany in July 2006.The 11 revised full papers presented were carefully reviewed and selected from 41 submissions.
Detection of intrusions and malware, and vulnerability assessment ; 17th International Conference, DIMVA 2020, Lisbon, Portugal, June 24–26, 2020, Proceedings
This book constitutes the proceedings of the 17th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2020, held in Lisbon, Portugal, in June 2020.* The 13 full papers presented in this volume were carefully reviewed and selected from 45 submissions. The contributions were organized in topical sections named: vulnerability discovery and analysis; attacks; web security; and detection and containment.
Deepfake detection = اكتشاف التزييف العميق
In the rapidly evolving era of artificial intelligence, addressing the escalating threats of deepfake technology becomes a necessity because of the increasing sophistication of AI algorithms in generating deceptive content, and since it threatens the integrity of information across diverse data. The main objective is to build a sophisticated AI-driven system to detect different types of deepfake in text, audio, and images. In English text deepfake detection, multiple pre-trained tokenizers have been used, but XLNET and BERT stand out with identifying objects outside the dataset with an accuracy of 0.9809 and both have been generalized & trained using LSTM. In Arabic text deepfake detection, Arabert has been trained using LSTM which led with an accuracy of 99.53% by generalizing the model. Both English and Arabic datasets have been generated to enhance the accuracy and effectiveness of the models. Audio deepfake detection has been generalized too, using Random Forest with an accuracy of 98.259%.
Deepfake detection
Recently, various techniques of manipulating the video content have become available to everyone – online, one can find free applications e.g., for face swapping in videos. Such universal accessibility carries a notable risk of flooding online content with false information, affecting not only the greats of this world, but also the whole societies, also the rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. It is therefore necessary to develop a verification tool that will help assess the authenticity of the videos posted on the internet. This project describes the approach of using artificial intelligence solutions to detect doctored videos.
Deepfake detection
The rise of large language models (LLMs) and the increasing sophistication of deepfake images have made detecting synthetic content a pressing challenge. Several approaches have been proposed to tackle this problem, including statistical analysis, and machine learning algorithms. In this project, A novel zero-shot approach is proposed that utilizes the power of LLMs to detect fake text. The pre-trained LLM is fine-tuned to enhance its ability to differentiate real and fake text. The approach uses the LLM to detect text by analyzing the log probabilities of the text. For detecting fake images, computer vision algorithms and neural networks are used to analyze facial features. The facial region is cropped and preprocessed and the neural network identifies patterns indicative of synthetic content.
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 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.
