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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Decoder-Only Transformers: The Brains Behind Generative AI, Large Language Models and Large Multimodal Models

Decoder-Only Transformers: The Brains Behind Generative AI, ...

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2024 International Conference on Computing, Communication, Cybersecurity and AI

作者： Naik, Dishita Naik, Ishita Naik, Nitin Birmingham City Univ Birmingham W Midlands England King Edward VI Five Ways Sch Birmingham W Midlands England Aston Univ Sch Comp Sci & Digital Technol Birmingham W Midlands England

ISBN: (纸本)9783031744426;9783031744433

The rise of creative machines is attributed to generative AI which enabled machines to create new contents. Wherein the introduction of the advanced neural network architecture known as a transformer revolutionized the landscape of generative AI. A transformer transforms one sequence into another sequence, and is primarily used in natural language processing and computer vision tasks. Which determines the relationship between tokens or words in a sequence to understand the context, while processing these tokens or words simultaneously. Transformers were built to resolve various issues of its previous neural networks, such as a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM);and are now the brains behind majority of generative AI models, for example Large Language Models (LLMs) and Large Multimodal Models (LMMs). This paper will explain about the transformer and its architectural components and working. Subsequently, it will illustrate the decoder-only transformer architecture and its components and working including the reason why this type of transformer architecture is used in most generative AI models such as LLMs and LMMs.

关键词： Transformer Attention Encoder Decoder GPT Generative Pre-trained Transformer Unsupervised Pre-training SFT Supervised Fine-tuning LLMs Large Language Models LMMs Large Multimodal Models NLP Natural Language Processing GenAI Generative AI

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Integrating an Advanced Evolutionary Intelligence System into the Internal Contracting Operations at Centro Hospitalar Universitario Lisboa Central 12th

Integrating an Advanced Evolutionary Intelligence System int...

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： Castro, Joao Melo E. Neves, Jose Magalhaes, Teresa Reis, Magda Univ Nova Lisboa Escola Nacl Saude Publ Lisbon Portugal Univ Minho Algoritmi Ctr LASI Braga Portugal CESPU IA & SAUDE IPSN Famalicao Portugal Ctr Hosp Univ Lisboa Cent Lisbon Portugal

ISBN: (纸本)9783031602207;9783031602214

The research outlined aims to advance and integrate an Evolutionary Intelligence Framework within the Internal Contracting Unit of Centro Hospitalar Universitario Lisboa Central (CHULC). This initiative is expected to streamline and amplify the effectiveness of the hospital's internal processes, especially concerning Outpatient Consultations and Surgical Operations. By adopting this technology, enhanced analytical insights will be accessible to middle management, including hospital administrators and department heads, and subsequently to the CHULC Board of Directors. Such insights will be instrumental for CHULC in optimizing its operational capacities in accordance with the anticipated volume of healthcare activities, and in identifying any underutilized resources or services.

关键词： Evolutionary Intelligence Artificial Intelligence Healthcare management

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Image Denoising with Diversity Enhanced Conditional GAN

Image Denoising with Diversity Enhanced Conditional GAN

引用

Computing Conference

作者： Kaczmar-Michalska, Joanna Rzepiela, Andrzej J. Wroclaw Univ Sci & Technol Dept Comp Sci Wroclaw Poland Swiss Fed Inst Technol Sci Ctr Opt & Electron Microscopy Zurich Switzerland

ISBN: (纸本)9783031622830;9783031622816;9783031622809

Image denoising is an inverse problem with many possible solutions. There have been many attempts to solve this problem, some producing single output, some a distribution of possible images. Among others, methods based on diffusion models and Generative Adversarial networks (GANs) generate high-quality denoised images. GANs are particularly useful in high throughput applications due to their computational efficiency. Still, they often suffer from mode collapse and cannot provide diverse distributions of denoised images. In this work, we present a simple variance-enhanced conditional GAN denoising model. Training with additional mean stabilizing and variance-enhancing loss terms results in a model which outperforms standard methods on three simple data sets and is robust to the hyperparameter tuning. We show that this architecture is well suited for (high-throughput) setups where only noisy target images are present e.g. single particle electron microscopy (EM) image denoising. Finally, we show that in the case of noisy targets, best-denoised predictions can be selected by posterior sampling to enhance further processing steps.

关键词： Denoising Noise-to-noise Image restoration GANs

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Enhancing Medical Diagnosis Through Deep Learning and Machine Learning Approaches in Image Analysis

Enhancing Medical Diagnosis Through Deep Learning and Machin...

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Intelligent systems Conference (IntelliSys)

作者： Usmani, Usman Ahmad Happonen, Ari Watada, Junzo Univ Teknol Petronas 79 LakeVille Seri Iskandar 32610 Perak Malaysia LUT Univ Yliopistonkatu 34 Lappeenranta 53850 Finland 1 Chome 104 Totsukamachi Shinjuku Tokyo 1698050 Japan

ISBN: (纸本)9783031477171;9783031477188

Medical imaging analysis plays a critical role in the medical field, transforming how diseases are found, diagnosed, and treated. The integration of machine learning and deep learning has dramatically advanced the field of medical image analysis, leading to the creation of more advanced algorithms for improved diagnosis and disease detection. This study examines the impact of these cutting-edge technologies on the accuracy of medical imaging analysis. It investigates the most effective algorithms and techniques currently used, as well as how different types of medical images impact the accuracy and efficiency of these algorithms. The limitations and challenges faced during implementation and their effect on healthcare professionals' decision-making are also explored. This research provides a comprehensive understanding of the state of the art in medical image analysis through machine learning and deep learning, highlighting recent developments and their practical applications.

关键词： Medical diagnosis Image analysis Radiology Pathology Machine learning Computer-Aided diagnosis Deep learning Artificial intelligence Imaging modalities Digitalizatio Ethical data analysis Smart society

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Developing 3D Welding Process with SIEMENS NX and KUKA Robot Manipulator 10th

Developing 3D Welding Process with SIEMENS NX and KUKA Robot...

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Belsak, Rok Plevcak, Robert Gotlih, Janez Vuherer, Tomaz Ficko, Mirko Karner, Timi Univ Maribor Fac Mech Engn Maribor 2000 Slovenia

ISBN: (纸本)9783031662676;9783031662683

Additive manufacturing (AM) has brought significant changes to the manufacturing industry. Among various AM methods, Wire Arc Additive Manufacturing (WAAM) is known for its cost-effectiveness and speed in creating metal structures. This paper discusses the development and initial testing of WAAM on a robotic system using Siemens NX. The system converts a CAD model into G-code and translates it to KUKA Robotic Language (KRL).This results in a complex welding trajectory useful for high quality prototype products or additional welding on semi-finished products.

关键词： Robot welding WAAM SIMENS NX 3D welding MIG/MAG

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Overview of Smart Parking: Types, Sensors Technologies and Energy Efficiency Strategies 4th

Overview of Smart Parking: Types, Sensors Technologies and E...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Alaoui, Hind Kadim Coulibaly, Moussa Errami, Ahmed II Univ Casablanca ENSEM LRILab NEST Res Grp BP 8118 Oasis Casablanca Hassan Morocco

ISBN: (纸本)9783031686528;9783031686535

As we realize today, especially in metropolitan cities, locating a proper parking space for a car is the biggest hassle. The time wasted searching for a parking space amplifies the already existing problems of congestion and leads to consumption of an unnecessary amount of fuel. This problem can be solved by using advanced communication technology known as smart parking systems. These systems use sophisticated sensing techniques to inform drivers, in real terms, of the availability of parking spaces. This paper presents a comprehensive study of various smart parking elements with a focus on the sensing side of smart parking systems that helps in detecting the presence of vehicles in various parking situations, it also clearly defines the classification of parking spots by differentiating between open-air and enclosed facilities, and highlight methods to reduce sensor energy consumption in order to ensure the smart parking infrastructure operates sustainably and effectively.

关键词： Sensors WSN Smart parking Energy guidance

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Improving Medication Prescription Strategies for Discordant Chronic Comorbidities Through Medical Data Bench-Marking and Recommender systems

Improving Medication Prescription Strategies for Discordant ...

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Computing Conference

作者： Ongwere, Tom Rutuja, Nimbalkar Nguyen, Tam, V Univ Dayton Dayton OH 45469 USA Amazon Web Serv Seattle WA USA

ISBN: (纸本)9783031622793;9783031622779;9783031622762

Patients with Discordant Chronic Comorbidities (DCCs) face the challenge of managing evolving treatment needs and conflicting regimens, necessitating multiple medical appointments. The complexity arises from conflicting treatment plans and potential drug interactions, making it difficult for both patients and healthcare providers to set an optimal treatment plan. To address this, Machine Learning-powered tools offer significant solution by providing treatment recommendations and detecting potential interactions. Despite the potential, there is a lack of comprehensive documentation on how these algorithms effectively handle conflicting recommendations and evolving patient needs in the context of DCCs. This research seeks explore and improve the how Support Vector Machines (SVM), Neural networks, and Random Forests algorithms set and prioritize treatment regimens for DCCs. We conducted survey to understand healthcare providers' strategies for prioritizing medication regimens, we constructed a dataset from survey data and deployed machine learning algorithms to predict optimal medication regimens for DCC patients with specific treatment concerns. Following benchmarking, SVM emerged as the top performer, achieving an impressive accuracy rate of 0.95.

关键词： Complex chronic diseases Discordant chronic conditions Bench-marching Recommender systems Machine learning Decision aids

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Models of Resilient systems with Online Verification Considering Changing Requirements and Latent Failures 1

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19th International Conference on Dependability of Computer systems (DepCoS-RELCOMEX)

作者： Kharchenko, Vyacheslav Ponochovnyi, Yuriy Dotsenko, Sergiy Illiashenko, Oleg Ivasiuk, Oleksandr Natl Aerosp Univ KhAI Kharkiv Ukraine Poltava State Agr Univ Poltava Ukraine Ukrainian State Univ Railway Transport Kharkiv Ukraine Natl Res Council IIT CNR Inst Informat & Telemat Pisa Italy

ISBN: (数字)9783031618574

ISBN: (纸本)9783031618567;9783031618574

The paper presents the outcomes of developing and implementing a cybernetic approach to creating and deploying resilient systems. It discusses the concepts and principles of real-time evolution for resilient systems, considering changing requirements, environments, and latent failures. This increased resilience stems from the necessity of integrating additional channels into the control system to address shifts in requirements, environments, or unspecified faults and failures. The general structure of resilient systems is founded on the principle of partitioning and managing channels, as well as system reconfiguration based on functional and non-functional characteristics. A case study of a space resilient system with online verification is presented. Three scenarios of system behavior to ensure resilience are proposed, considering various tolerance and management mechanisms. The paper analyzes the results of developing and researching Markov models for these scenarios, offering insights for enhancing the availability of resilient systems.

关键词： Information and Control System Security Safety Resilience Online Verification

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Machine Learning Algorithm for Cancer Prediction: A Bibliometric Review

Machine Learning Algorithm for Cancer Prediction: A Bibliome...

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Intelligent systems Conference

作者： Ampofo, Isaac Atta Senior Abdulai, Mobarik Karikari, Maxwell Twumasi, Lydia Ampofo, Isaac Atta Junior Ampofo, Beatrice Univ Liverpool Brownlow Hill Liverpool L69 7ZX Merseyside England Univ Educ Winneba Ghana Kwame Nkrumah Univ Sci & Technol Kumasi Ghana Aketen Appaih Menka Univ Skills Training & Entrep 1277 IPT Rd Kumasi Ghana

ISBN: (纸本)9783031664274;9783031664281

The incidence of cancer is rising dramatically on a global scale. Many people receive a late-stage diagnosis despite though cancer is preventable and well-treated in its early stages. Furthermore, cancer frequently returns following protracted treatment. Therefore, it is essential to anticipate cancer recurrence to proactively pursue specific treatments. The study review literature done on machine learning algorithm for cancer prediction using bibliometric review and Vosviewer. The studies that were taken into consideration for this research were obtained from the Web of Science database. A range of search terms, such as pertinent keywords (Machine learning, cancer detection, cancer prediction), were utilized to examine the title, keywords, and abstract of articles inside the database covering the years 2014 through 2023. 1914 documents were used for the study. Articles published in IEEE Access have a greater impact, but Scientific Reports had received more citations than articles in other journals-roughly 1018 of all the citations for the chosen documents come from articles published in this journal. Research on International evaluation of an AI system for screening of breast cancer had the highest citations 1059. India has the highest documents of 430 representing 22.419%. For citations, the USA has the highest of 122,072. Egyptian Knowledge Bank EKB has the highest documents of 61 representing 3.180%. National Natural Science Foundation of China (NSFC) has the highest documents of 131 representing 6.830%. Machine learning must be incorporated into clinical practice and trials due to the changing nature of cancer and how it is treated over time.

关键词： Cancer Bibliometric review Prediction VoS viewer Machine learning algorithm

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Application of Artificial Intelligence on Camera-Based Human Pose Prediction for Yoga: A Methodological Study 1

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International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Kavuri, Radhika Bangari, Meghana Yarram, Harika Talari, Harika Chaitanya Bharathi Inst Technol Hyderabad India

ISBN: (数字)9789819984763

ISBN: (纸本)9789819984756;9789819984763

Due to the physical, mental, and psychological advantages yoga offers the human body, it has evolved into the main activity of the majority of people. Artificial intelligence is developing quickly, and this technology is quickly replacing human labor in a variety of industries, including manufacturing and self-driving cars. This raises the possibility of AI's development in. A lot of attention has been given to Human Pose Estimation (HPE) in recent years due to the use of deep learning to increase its performance and introduce intriguing new applications, especially in the field of physical activity, bringing us the application of yoga. Reviewing existing research on HPE's use in yoga is the goal of this study, along with looking at current data and methods, and potential future applications. The paper also presents the performance of the Yoga-82 dataset on the CNN model we have built which achieved the accuracy of 81.59% and compares to previous works. This study and research has been done on the data that is available publicly and has been sourced from online resources and references mentioned.

关键词： Artificial intelligence Computer vision Pose estimation Human joint detection Key points Yoga pose CNN MediaPipe

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