Computer & IT

L'aumento degli incidenti da incendio ha reso i sistemi di allarme antincendio una componente cruciale degli accessori necessari per qualsiasi tipo di costruzione. Le tragedie che si verificano con maggiore frequenza oggi sono gli incidenti da incendio. Sono state proposte numerose tecniche per il rilevamento precoce degli incendi, al fine di ridurne la frequenza e i danni.Vi presentiamo Flamed, un modello di deep learning all'avanguardia per il rilevamento istantaneo di incendi e fumo. Quando si tratta di individuare possibili minacce di incendio, la tecnologia basata sulle reti neurali convoluzionali (CNN) di Flamed, addestrata su un consistente set di dati di Kaggle, offre una precisione e una velocità senza pari. Il design all'avanguardia e la velocità di elaborazione di Flamed consentono di analizzare rapidamente i flussi video in diretta e di rilevare fiamme e fumo con una precisione senza pari. Flamed è l'opzione migliore per il rilevamento precoce degli incendi negli edifici, il monitoraggio degli ambienti industriali e il mantenimento della sicurezza pubblica. Contate su Flamed per ricevere notifiche accurate e veloci per proteggere le persone e le proprietà dall'impatto distruttivo del fuoco e del fumo.

El aumento de los accidentes de incendio ha convertido a los sistemas de alarma contra incendios en un componente crucial de los accesorios necesarios en cualquier tipo de construcción. Las tragedias que ocurren con más frecuencia hoy en día son los accidentes por incendio. Se han sugerido numerosas técnicas para la detección temprana de incendios con el fin de disminuir su frecuencia y los daños que causan.Presentamos Flamed, un modelo de aprendizaje profundo de última generación para la detección instantánea de fuego y humo. Cuando se trata de detectar posibles riesgos de incendio, la tecnología basada en redes neuronales convolucionales (CNN) de Flamed, que fue entrenada en un importante conjunto de datos de Kaggle, proporciona una precisión y velocidad inigualables. El diseño vanguardista de Flamed y su rápida velocidad de procesamiento le permiten analizar rápidamente secuencias de vídeo en directo y detectar llamas y humo con una precisión inigualable. Flamed es la mejor opción para la detección temprana de incendios en edificios, la supervisión de entornos industriales y el mantenimiento de la seguridad pública. Cuente con Flamed para proporcionar notificaciones precisas y rápidas con el fin de proteger a las personas y los bienes de los efectos destructivos del fuego y el humo.

Las técnicas de aprendizaje automático y aprendizaje profundo (DL) han mostrado resultados prometedores en la detección de actividades fraudulentas. En esta tesis, proponemos enfoques para la detección de fraudes con tarjetas de crédito que combinan técnicas de aprendizaje supervisado y no supervisado. Aplicamos técnicas de ingeniería de características para extraer características relevantes del conjunto de datos de transacciones de tarjetas de crédito, seguidas de modelos de detección de anomalías que combinan ML supervisado, ML semisupervisado y técnicas de DL. Analizamos el conjunto de datos utilizando varios parámetros y métodos. Nuestro estudio sobre varios métodos ML y DL en la detección de transacciones fraudulentas son Redes Neuronales Artificiales, Redes Neuronales Convolucionales, Clasificador de Vectores de Soporte con Autoencoder, K-Nearest XGBoost, CatBoost, Adaboost, Gradient Boosting, Random Forest, Decision Tree, K-Means Clustering, LightBGM, Logistic Regression, regresión logística con datos submuestreados, Naive Bayes logra, SVC logra, Isolation Forest y Local Outlier Factor. Evaluamos nuestro enfoque en un conjunto de datos de transacciones de tarjetas de crédito del mundo real llamado Creditcard.csv del conjunto de datos Kaggle.

V swqzi s uchastiwshimisq sluchaqmi pozharow sistemy pozharnoj signalizacii stali wazhnejshim komponentom, neobhodimym dlq lübogo tipa stroitel'stwa. Tragedii, kotorye proishodqt segodnq chasche wsego, - äto pozhary. Chtoby snizit' chastotu pozharow i uscherb ot nih, bylo predlozheno mnozhestwo metodow rannego obnaruzheniq wozgoranij.Predstawlqem Flamed, sowremennuü model' glubokogo obucheniq dlq mgnowennogo obnaruzheniq ognq i dyma. Tehnologiq Flamed, osnowannaq na konwolücionnyh nejronnyh setqh (CNN) i obuchennaq na bol'shom nabore dannyh Kaggle, obespechiwaet neprewzojdennuü tochnost' i skorost' obnaruzheniq wozmozhnyh ugroz pozhara. Peredowaq razrabotka i wysokaq skorost' obrabotki dannyh pozwolqüt Flamed bystro analizirowat' wideopotok w real'nom wremeni i obnaruzhiwat' plamq i dym s neprewzojdennoj tochnost'ü. Flamed - luchshij wariant dlq rannego obnaruzheniq pozhara w zdaniqh, monitoringa promyshlennyh ob#ektow i obespecheniq obschestwennoj bezopasnosti. Polozhites' na Flamed w obespechenii tochnyh i bystryh uwedomlenij dlq zaschity lüdej i imuschestwa ot razrushitel'nogo wozdejstwiq ognq i dyma.

Metody mashinnogo obucheniq i glubokogo obucheniq (DL) pokazali mnogoobeschaüschie rezul'taty w obnaruzhenii moshennicheskih dejstwij. V ätoj dissertacii my predlagaem podhody k obnaruzheniü moshennichestwa s kreditnymi kartami, kotorye sochetaüt w sebe kontroliruemye i nekontroliruemye metody obucheniq. My primenqem metody proektirowaniq funkcij dlq izwlecheniq sootwetstwuüschih funkcij iz nabora dannyh tranzakcij po kreditnym kartam, a zatem modeli obnaruzheniq anomalij, kotorye sochetaüt w sebe metody kontroliruemogo ML, polukontroliruemogo ML i DL. My analiziruem nabor dannyh, ispol'zuq razlichnye parametry i metody. Nashe issledowanie razlichnyh metodow ML i DL dlq obnaruzheniq moshennicheskih tranzakcij: iskusstwennye nejronnye seti (ANN), swertochnye nejronnye seti (CNN), linejnaq regressiq s awtoänkoderom, logisticheskaq regressiq s nedostatochnoj wyborkoj dannyh, dostizheniq naiwnogo Bajesa, dostizheniq SVC, izolqcionnyj les i lokal'nyj faktor wybrosow. My oceniwaem nash podhod na real'nom nabore dannyh o tranzakciqh po kreditnym kartam s imenem Creditcard.csv iz nabora dannyh Kaggle.

Die Zunahme von Brandunfällen hat Brandmeldeanlagen zu einem wichtigen Bestandteil des notwendigen Zubehörs für jede Art von Bauwerk gemacht. Die Tragödien, die sich heute am häufigsten ereignen, sind Brandunfälle. Es wurden zahlreiche Techniken zur Brandfrüherkennung vorgeschlagen, um die Häufigkeit von Brandfällen und die dadurch verursachten Schäden zu verringern.Wir stellen Flamed vor, ein hochmodernes Deep-Learning-Modell für die sofortige Erkennung von Feuer und Rauch. Wenn es darum geht, mögliche Brandgefahren zu erkennen, bietet die auf Convolutional Neural Networks (CNN) basierende Technologie von Flamed, die auf einem umfangreichen Kaggle-Datensatz trainiert wurde, unübertroffene Genauigkeit und Geschwindigkeit. Das hochmoderne Design und die hohe Verarbeitungsgeschwindigkeit von Flamed ermöglichen die schnelle Analyse von Live-Videobildern und die Erkennung von Flammen und Rauch mit unübertroffener Genauigkeit. Flamed ist die beste Option für die Brandfrüherkennung in Gebäuden, die Überwachung industrieller Umgebungen und die Aufrechterhaltung der öffentlichen Sicherheit. Verlassen Sie sich auf Flamed, wenn es darum geht, präzise und schnelle Benachrichtigungen zu liefern, um Menschen und Eigentum vor den zerstörerischen Auswirkungen von Feuer und Rauch zu schützen.

Techniken des maschinellen Lernens und des Deep Learning (DL) haben vielversprechende Ergebnisse bei der Erkennung betrügerischer Aktivitäten gezeigt. In dieser Arbeit schlagen wir Ansätze zur Erkennung von Kreditkartenbetrug vor, die überwachte und nicht überwachte Lerntechniken kombinieren. Wir wenden Feature-Engineering-Techniken an, um relevante Merkmale aus dem Datensatz für Kreditkartentransaktionen zu extrahieren, gefolgt von Modellen zur Erkennung von Anomalien, die überwachte ML-, semi-supervised ML- und DL-Techniken kombinieren. Wir analysieren den Datensatz mit verschiedenen Parametern und Methoden. Unsere Studie über verschiedene ML- und DL-Methoden zur Erkennung betrügerischer Transaktionen umfasst künstliche neuronale Netze (ANN), XGBoost, CatBoost, Adaboost, Gradient Boosting, Random Forest, Entscheidungsbaum, K-Means Clustering, LightBGM, logistische Regression, logistische Regression mit unterabgetasteten Daten, Naive Bayes achieves, SVC achieves, Isolation Forest und Local Outlier Factor. Wir evaluieren unseren Ansatz anhand eines realen Kreditkarten-Transaktionsdatensatzes namens Creditcard.csv aus dem Kaggle-Datensatz.

O aumento do número de acidentes com incêndios fez com que os sistemas de alarme de incêndio se tornassem um componente crucial dos acessórios necessários em qualquer tipo de construção. As tragédias mais frequentes atualmente são os acidentes de incêndio. Foram sugeridas várias técnicas para a deteção precoce de incêndios, a fim de diminuir a frequência dos incidentes de incêndio e os danos que causam.Apresentamos o Flamed, um modelo de aprendizagem profunda de última geração para a deteção instantânea de incêndios e fumo. Quando se trata de detetar possíveis ameaças de incêndio, a tecnologia baseada em redes neurais convolucionais (CNN) do Flamed, que foi treinada em um conjunto de dados substancial do Kaggle, oferece precisão e velocidade incomparáveis. O design de ponta do Flamed e a velocidade de processamento rápida permitem que ele analise rapidamente as transmissões de vídeo ao vivo e detecte chamas e fumaça com precisão incomparável. O Flamed é a melhor opção para deteção precoce de incêndios em edifícios, monitoramento de ambientes industriais e manutenção da segurança pública. Conte com o Flamed para fornecer notificações precisas e rápidas para proteger pessoas e propriedades dos impactos destrutivos do fogo e da fumaça.

As técnicas de aprendizagem automática e de aprendizagem profunda (DL) têm mostrado resultados promissores na deteção de actividades fraudulentas. Nesta tese, propomos abordagens para a deteção de fraudes com cartões de crédito que combinam técnicas de aprendizagem supervisionadas e não supervisionadas. Aplicamos técnicas de engenharia de características para extrair características relevantes do conjunto de dados de transacções com cartões de crédito, seguidas de modelos de deteção de anomalias que combinam ML supervisionado, ML semi-supervisionado e técnicas DL. Analisamos o conjunto de dados utilizando vários parâmetros e métodos. O nosso estudo sobre vários métodos de ML e DL na deteção de transacções fraudulentas inclui Redes Neuronais Artificiais (ANN), Redes Neuronais Convolucionais (CNN), Regressão Linear com Autoencoder, K-Nearest Neighbors (KNN), XGBoost, CatBoost, Adaboost, Gradient Boosting, Random Forest, Árvore de Decisão, K-Means Clustering, LightBGM, Regressão Logística, regressão logística com dados subamostrados, Naive Bayes alcança, SVC alcança, Isolation Forest e Local Outlier Fator. Avaliamos a nossa abordagem num conjunto de dados de transacções com cartões de crédito do mundo real denominado Creditcard.csv do conjunto de dados Kaggle.

Generative Artificial Intelligence is rapidly advancing with many state-of-the-art performances on computer vision, speech processing, and natural language processing tasks. Generative adversarial networks and neural diffusion models can generate high-quality synthetic images of human faces, artworks, and coherent essays on different topics. Generative models are also transforming Medical Artificial Intelligence, given their potential to learn complex features from medical imaging and healthcare data. Hence, computer-aided diagnosis and healthcare are benefiting from Medical Artificial Intelligence and Generative Artificial Intelligence. This book presents the recent advances in generative models for Medical Artificial Intelligence. It covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. This book highlights the recent advancements in Generative Artificial Intelligence for medical and healthcare applications, using medical imaging and clinical and electronic health records data. Furthermore, the book comprehensively presents the concepts and applications of deep learning-based artificial intelligence methods, such as generative adversarial networks, convolutional neural networks, and vision transformers. It also presents a quantitative and qualitative analysis of data augmentation and synthesis performances of Generative Artificial Intelligence models. This book is the result of the collaborative efforts and hard work of many minds who contributed to it and illuminated the vast landscape of Medical Artificial Intelligence. The book is suitable for reading by computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence in healthcare. It serves as a compass for navigating the artificial intelligence-driven healthcare landscape.

Das Vertrauen in die Infrastruktur in Deutschland hat seit der Hochwasserkatastrophe im Ahrtal 2021 stark abgenommen. Zwischenzeitlich hat sich aber auch nicht viel verändert: Auf kommunaler Ebene steht Überforderung an der Tagesordnung- thematisch wie finanziell. Die Konsequenzen sehen wir jeden Tag: kaputte Straßen und Brücken, sanierungsbedürftige Anlagen, Investitionsstau und vieles mehr. Ändern wir nichts daran, sind neue Katastrophen unabwendbar.Infrastrukturexperte Markus Becker hat die Flutkatastrophe im Ahrtal selbst als Familienvater sowie Mitglied des Kristenstabs erlebt. Zusammen mit dem Wachstumsexperten Guido Quelle zeigt er anhand der Ahrtalkatastrophe auf, was (Ober-)Bürgermeister, Landräte und Führungskräfte in Kommunalverwaltungen und kommunalen Betriebe, aber auch in privaten Infrastrukturunternehmen tun können, um eine widerstandsfähige Infrastruktur zu fördern und aufzubauen.

Projektarbeit aus dem Jahr 2022 im Fachbereich Informatik - Künstliche Intelligenz, Note: 1,0, SRH Fernhochschule, Veranstaltung: Qualitative Datenanalyse, Sprache: Deutsch, Abstract: Die vorliegende Arbeit beschäftigt sich mit der Forschung und Entwicklung im Bereich der KI. Aufgrund dieses sehr umfassenden Themengebietes wird dabei ein besonderer Fokus auf den Bereich Maschinenethik gelegt, welcher tiefgehender beleuchtet wird. Maschinenethik beschäftigt sich mit ethischen Fragestellungen bezüglich autonomer Maschinen mit moralischen Fähigkeiten und deren Konturierung und befindet sich demnach in einem Überlagerungsbereich der wissenschaftlichen Forschung künstlicher Intelligenz, sowie der Debatte der ethischen Einflussfaktoren, woraus sich ethische Fragestellungen ergeben. Die Arbeit nimmt Bezug auf die Frage, wie viel moralische Entscheidungsfreiheit eine Maschine haben sollte und ob die Chancen, den Risiken überwiegen können. Diese Fragen werden mit Hilfe einer Dokumentenanalyse des im Jahr 2018 in der Zeitschrift ¿Aus Politik und Zeitgeschichte¿ veröffentlichten Textbeitrags ¿Maschinenethik und ¿Artificial Morality¿: Können Maschinen moralisch handeln?¿ untersucht.

Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycleKey Features:Interpret your models' decision-making process, ensuring transparency and trust in your AI-powered solutionsGain hands-on experience in every step of the deep learning life cycleExplore case studies and solutions for deploying DL models while addressing scalability, data drift, and ethical considerationsPurchase of the print or Kindle book includes a free PDF eBookBook Description:Deep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives.This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You'll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency.As you progress, you'll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You'll also discover the transformative potential of large language models (LLMs) for a wide array of applications.By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.What You Will Learn:Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs)Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your modelDeal with multi-modal data drift in a production environmentEvaluate the quality and bias of your modelsExplore techniques to protect your model from adversarial attacksGet to grips with deploying a model with DataRobot AutoMLWho this book is for:This book is for deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book.

Gain practical, recipe-based insights into the world of deep learning using Apache MXNet for flexible and efficient research prototyping, training, and deployment to production.Key Features:A step-by-step tutorial towards using MXNet products to create scalable deep learning applicationsImplement tasks such as transfer learning, transformers, and more with the required speed and scalabilityAnalyze the performance of models and fine-tune them for accuracy, scalability, and speedBook Description:MXNet is an open-source deep learning framework that allows you to train and deploy neural network models and implement state-of-the-art (SOTA) architectures in CV, NLP, and more. With this cookbook, you will be able to construct fast, scalable deep learning solutions using Apache MXNet.This book will start by showing you the different versions of MXNet and what version to choose before installing your library. You will learn to start using MXNet/Gluon libraries to solve classification and regression problems and get an idea on the inner workings of these libraries. This book will also show how to use MXNet to analyze toy datasets in the areas of numerical regression, data classification, picture classification, and text classification. You'll also learn to build and train deep-learning neural network architectures from scratch, before moving on to complex concepts like transfer learning. You'll learn to construct and deploy neural network architectures including CNN, RNN, LSTMs, Transformers, and integrate these models into your applications.By the end of the book, you will be able to utilize the MXNet and Gluon libraries to create and train deep learning networks using GPUs and learn how to deploy them efficiently in different environments.What You Will Learn:Understand MXNet and Gluon libraries and their advantagesBuild and train network models from scratch using MXNetApply transfer learning for more complex, fine-tuned network architecturesSolve modern Computer Vision and NLP problems using neural network techniquesTrain and evaluate models using GPUs and learn how to deploy themExplore state-of-the-art models with GPUs and leveraging modern optimization techniquesImprove inference run-times and deploy models in production Who this book is for:This book is ideal for Data scientists, machine learning engineers, and developers who want to work with Apache MXNet for building fast, scalable deep learning solutions. The reader is expected to have a good understanding of Python programming and a working environment with Python 3.6+. A good theoretical understanding of mathematics for deep learning will be beneficial.

Dive into the future of blockchain with "Empowering a Unified Blockchain Future: Bridging the Gaps with Trustless Interoperability and Chain Keys." This groundbreaking exploration unveils the secrets behind seamless communication in the blockchain era, unraveling the potential of trustless interoperability and the game-changing role of Chain Keys.In the corridors of this book, discover the essence of trustless communication and the security advantages embedded in Chain Keys, the cryptographic architects of a new digital era. Bridges spanned between blockchains, orchestrated by oracles and relayers, redefine the landscape of decentralized collaboration. A comparative analysis demystifies the pros and cons of interoperability approaches, providing a compass to navigate the delicate trade-offs between security, trustlessness, scalability, and user experience.Enter the realm of trustless paradigms, eliminating reliance on trusted third parties and addressing concerns surrounding Chain Keys. This journey not only makes a compelling case for trustless interoperability but also envisions its impact on the ever-evolving blockchain ecosystem. Real-world applications showcase the transformative power of Chain Keys, paving the way for a future where interoperable blockchain ecosystems shape industries, finance, and the way we connect in the digital universe.Get ready for an odyssey into the heart of blockchain innovation-an odyssey that empowers you to envision and contribute to the unified blockchain future.

The Human Edge in the AI Age: Risk Mitigation and Strategic CollaborationCONVERSATIONAL CHAT INFORMATIVE BOOKBy ABEBE- BARD AI WOLDEMARIAM "The Human Edge in the AI Age: Risk Mitigation and Strategic Collaboration" by Abebe Bard AI Woldemariam is an insightful exploration of the transformative landscape presented by the rise of Artificial Intelligence (AI). The book offers a comprehensive framework for navigating this new era with caution and o`ptimism, emphasizing the need for strategic collaboration between humans and AI. It reframes the narrative from humans versus AI to a collaborative future, highlighting the potential for solving global challenges, building a more sustainable world, and creating a more equitable society. The book covers topics such as understanding the AI landscape and its challenges, risk mitigation strategies, and the importance of strategic collaboration. It also delves into the significance of leveraging human strengths, fostering human-AI partnerships, and promoting a shared vision for the future of AI. With a focus on acknowledging risks, implementing effective mitigation strategies, and fostering collaboration, the book aims to guide readers towards a shared future of prosperity and progress.Additionally, the book includes case studies, essays, literature, research, practices, experiences, trends and reports related to the subject matter.

Over the past two decades, China has emerged as a frontrunner in global business innovation. Unleashing Innovation: Ten Cases from China on Digital Strategy and Market Expansion tells the inside stories behind eleven pioneering companies in the Chinese market that are rewriting the rules of business. Produced by Cheung Kong Graduate School of Business, the book dissects how leading enterprises across e-commerce, technology, manufacturing, and consumer products are pioneering advances in digital transformation, global expansion, and supply chain innovation in the Chinese market.¿ Learn how Alibaba and JD.com have been leading the way in e-commerce with contrasting business models and strategies.¿ Discover how manufacturers like NIO and TCL are revolutionizing their supply chains and products.¿ Understand the global growth strategies behind breakout Chinese brands like SHEIN and dairy producer Yili.¿ Examine how Western brands like Swedish oat milk maker Oatly and American fast food giant KFC tailored themselves to achieve success in China.Unleashing Innovation draws on in-depth case studies co-authored by CKGSB's professors and researchers. The book is ideal for business leaders, investors, policymakers, and anyone seeking an insiders' perspective on business strategy in China.

This book contains papers presented at the 2nd International Conference on Cognitive based Information Processing and Applications (CIPA) in Changzhou, China, from September 22 to 23, 2022. The book is divided into a 2-volume series and the papers represent the various technological advancements in network information processing, graphics and image processing, medical care, machine learning, smart cities. It caters to postgraduate students, researchers, and practitioners specializing and working in the area of cognitive-inspired computing and information processing.

Navigating the Nexus: AI, Ethics, and Ethiopian CultureCONVERSATIONAL CHAT INFORMATIVE BOOKBy ABEBE- BARD AI WOLDEMARIAM Weaving together ancient wisdom and cutting-edge technology, "Navigating the Nexus" explores how AI can be harnessed to empower Ethiopia, respecting its rich cultural tapestry. By embracing values like Ubuntu and Sankofa, the book guides us through ethical considerations, from data privacy to algorithmic fairness, ensuring AI serves the common good. It showcases AI applications tackling critical challenges in healthcare, education, and agriculture, while offering solutions to bridge infrastructure and expertise gaps. Ultimately, it emphasizes the importance of public trust and community engagement, paving the way for a future where AI thrives alongside a well-informed and empowered Ethiopian society.

Data analytics for the improvement of control by overspeed alert in the monitoring management center in SUTRAN Lima. Analyzing all data in real time, historical, structured, unstructured, qualitative leads to perform a data analytics in the monitoring management center for it in this report was to apply these tools to the data to improve the control of the activity developed by speeding alerts detected by the Fleet Monitoring Control System to transport vehicles of passengers and goods authorized by the Ministry of Transport circulating in the national roads of the Peruvian territory. The information used was obtained from previous years' files by the Supervisor of the monitoring management center and in coordination with the area's operating colleagues.

In Vehicular Ad Hoc Networks (VANETs) kommunizieren die Fahrzeuge über Vehicle-to-Vehicle (V to V) und Vehicle-to-Roadside Unit (V to RSU) Kanäle, was eine große Herausforderung für einen breiten Einsatz darstellt. Ein zentrales Problem ist die Entwicklung von Algorithmen zur Minimierung von Verzögerungen, um mit Pfadunterbrechungen aufgrund von Fahrzeugmobilität umzugehen. In dieser Dissertation wird ein Verfahren vorgeschlagen, das sich auf die Verringerung von Verzögerungen für Fahrzeuge mit hoher Geschwindigkeit konzentriert und das Kollisionsrisiko minimiert. Es nutzt Fahrzeuge mit geringer Geschwindigkeit, um Informationen über den Straßenzustand und die Geschwindigkeit auszutauschen und so den Gesamtverkehr in dynamischen VANETs zu reduzieren. Das AODV-Routing-Protokoll wird für überflutete Anfragen und Antworten eingesetzt, wobei spezielle Pakete mit verzögerungsrelevanten Informationen gesendet werden. Computersimulationen mit dem ns-2-Simulator zeigen die Effektivität des Schemas bei der Erhöhung der Informationsübermittlung, der Reduzierung des Paket-Overheads und der Verbesserung des Durchsatzes.

Das Management von Innovationsabläufen und -prozessen umfasst die Art und Weise, wie Betrieb und Produktion bestehende Aktivitäten ergänzen und Optionen für neue Innovationsaktivitäten bieten. Es interessiert sich für ein breites Spektrum unternehmerischer und organisatorischer Fragestellungen. Das Endziel des technologischen Innovationsmanagements ist der Prozess der Kommerzialisierung ¿ also der Erlöse aus Investitionen in Innovationen. Die Wertaneignung der Investitionen von Unternehmen in technologische Innovationen umfasst geistige Eigentumsrechte, die Gewährung von Lizenzen, die Schaffung technischer Standards, Geschwindigkeit, Geheimhaltung und den Besitz zusätzlicher Vorteile. Viele wichtige Aktivitäten, die von Verarbeitungsunternehmen, Marketing, Vertrieb, angewandter Technik, Design, Wartung und Buchhaltung durchgeführt werden, würden als Dienstleistungen bezeichnet, wenn sie extern angeboten würden. Viele Dienstleistungen wie die Bankabrechnung werden heute elektromechanisch angeboten und der Wert der Verarbeitungsprodukte liegt heute in immateriellen Eigenschaften wie schneller Lieferung, einfacher Bedienung, Marke und Zuverlässigkeit, die als Dienstleistungen betrachtet worden wären, wenn sie nicht in die Produkte eingebettet gewesen wären.