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

共 9542 条记录，以下是4311-4320 订阅

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Dynamic Traffic Optimization System: Leveraging IoT and Fog Computing for Enhanced Urban Mobility with the RAO Algorithm 7th

Dynamic Traffic Optimization System: Leveraging IoT and Fog ...

引用

7th International Conference on Green Technology and Sustainable Development

作者： Thinh Vinh Le Duy Lap Le Huan Thien Tran HCMC Univ Educ & Technol Ho Chi Minh City Vietnam Saigon Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9783031761966;9783031761973

This paper introduces a cutting-edge method for urban traffic management that harnesses the power of the RAO (named after Professor Ravipudi Venkata Rao) optimization algorithm, enhanced by the integration of the Internet of Things (IoT) and fog computing technologies. By collecting real-time traffic data from IoT devices, such as smart cameras and sensors, our approach provides a detailed and up-to-the-minute view of urban traffic conditions. This data is efficiently processed through fog computing infrastructure, which ensures minimal latency and supports immediate responses to fluctuating traffic patterns. The deployment of the RAO algorithm on this rich dataset stands out as a novel contribution, dynamically optimizing traffic flows and routes based on current conditions without the need for specific map data sources. Our research extends beyond traditional traffic management applications, offering significant benefits in emergencies by facilitating quicker medical and security responses and enhancing the management of military operations with the need for rapid and secure route planning. Through experimental simulations conducted in urban environments, our findings demonstrate substantial improvements in traffic management and congestion alleviation. The results indicate that our integrated strategy offers a viable and adaptable solution for both everyday traffic oversight and exceptional scenario planning. This study lays the groundwork for future advancements in smart city technologies, underscoring the role of sophisticated technological integration in fostering more sustainable, efficient, and secure urban landscapes.

关键词： Optimization RAO Dijkstra Traffic Fog computing IoT

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Machine Learning Predictive Model for Thyroid Disease Detection 13th

Machine Learning Predictive Model for Thyroid Disease Detect...

引用

13th Computer Science Online Conference

作者： Cuadros, Axel La Torre, Diego Ticona, Wilfredo Univ ESAN Lima Peru Univ Tecnol Peru Lima Peru

ISBN: (纸本)9783031702846;9783031702853

The thyroid, a butterfly-shaped gland located in the neck, plays a crucial role in regulating metabolism, energy, and hormonal balance, meeting the peripheral tissues' needs. Thyroid diseases pose a significant global health problem, affecting millions with varying degrees of severity. Conditions such as hyperthyroidism and hypothyroidism can manifest symptoms due to fluctuations in thyroid hormone levels, impacting individuals' well-being. Additionally, thyroid disorders may involve the enlargement of the gland, known as goiter, or the formation of thyroid nodules, which can have functional or neoplastic implications. This article explores the development and implementation of a predictive model using advanced Machine Learning techniques to forecast thyroid diseases. By analyzing clinical and relevant biomedical data, we employ sophisticated Machine Learning algorithms to identify hidden patterns and correlations within the data. The goal is to enhance precision and anticipation in diagnosing thyroid diseases, offering healthcare professionals a powerful tool for improved patient care, and optimized clinical outcomes. The results highlight that the Random Forest model achieved remarkable performance with 100% on the accuracy, precision, recall F1 Score and AUC metrics.

关键词： Thyroid diseases Machine Learning predictive modeling healthcare personalized medicine

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Interpretable Machine Learning Based Prediction of Bearing Capacity of Rectangular RC Columns 1

引用

16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Yu, Dehu Zhang, Shicheng Yang, Shujuan Wang, Chunhui Shandong Jianzhu Univ Jinan 250101 Peoples R China Qingdao Univ Technol Qingdao 266033 Shandong Peoples R China

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

Reinforced concrete (RC) columns are the main stress components in reinforced concrete structures;accurately obtaining the yield load and ultimate load of RC columns under low cyclic load can guarantee the safety of reinforced concrete structures and improve structural stability and reliability. Firstly, 160 sets of test data of RC columns were collected. Five machine learning algorithms, namely, Random Forest, AdaBoost, GBRT, XGBoost, and CatBoost, were used to establish the yield load and ultimate load prediction models, and the Bayesian optimisation algorithm was used to optimise the hyper-parameter combinations of the prediction models, to obtain the five prediction models under the optimal hyper-parameter combinations. Then, different evaluation indexes are used to compare the prediction results of the optimised prediction models horizontally, and the empirical formula is used to calculate the ultimate bearing capacity of the test set sample data;the calculated results are compared with the prediction results of the optimal model. Finally, the SHAP method gives the global and local interpretations of the optimal prediction models. The results show that among the five machine learning prediction models, the CatBoost algorithm-based prediction model outperforms the other prediction models and empirical formulas in four evaluation indexes, R2, MAE, MAPE, and MSE, in yield and ultimate load prediction, and R2 reaches 0.9347 for the yield load prediction model and 0.9382 for the ultimate load prediction model, the two prediction models have higher overall prediction accuracy. Among all the essential characteristics, the shear-to-span ratio, section height, axial compression ratio, and section width have a more significant influence on the predictions of yield and ultimate loads;the results of the study can provide a reference for the calculation of yield and ultimate loads of rectangular RC columns under low-cycle reversed loading.

关键词： Machine Learning RC Column Hyperparameter Optimization SHAP CatBoost

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Reactive Correction of Object Placement Errors for Robotic Arrangement Tasks 18th

Reactive Correction of Object Placement Errors for Robotic A...

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18th International Conference on Intelligent Autonomous systems (IAS)

作者： Kreis, Benedikt Menon, Rohit Adinarayan, Bharath Kumar de Heuvel, Jorge Bennewitz, Maren Univ Bonn Humanoid Robots Lab D-53115 Bonn Germany

ISBN: (纸本)9783031449802;9783031449819

When arranging objects with robotic arms, the quality of the end result strongly depends on the achievable placement accuracy. However, even the most advanced robotic systems are prone to positioning errors that can occur at different steps of the manipulation process. Ignoring such errors can lead to the partial or complete failure of the arrangement. In this paper, we present a novel approach to autonomously detect and correct misplaced objects by pushing them with a robotic arm. We thoroughly tested our approach both in simulation and on real hardware using a Robotiq two-finger gripper mounted on a UR5 robotic arm. In our evaluation, we demonstrate the successful compensation for different errors injected during the manipulation of regular shaped objects. Consequently, we achieve a highly reliable object placement accuracy in the millimeter range.

关键词： Robotic manipulation Assembly Feedback control Sim-to-real gap

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Breast Cancer Stage Determination Using Deep Learning 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Aniq, Elmehdi Chakraoui, Mohamed Mouhni, Naoual Aboulfalah, Abderrahim Rais, Hanane Sultan Moulay Slimane Univ Polydisciplinary Fac Khouribga LS3ME Beni Mellal Morocco EMSI Marrakech LAMIGEP Marrakech Morocco UMI Meknes Fac Sci & Tech Dept Informat GL ISI Team Errachidia Morocco Cadi Ayyad Univ Fac Med & Pharm Dept Obstet Gynecol Marrakech Morocco Cadi Ayyad Univ Fac Med & Pharm Dept Pathol Anat Marrakech Morocco

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

Ki-67 is a non-histone nuclear protein located in the nuclear cortex and is one of the essential biomarkers used to provide the proliferative status of cancer cells. Because of the variability in color, morphology and intensity of the cell nuclei Ki-67 is sensitive to chemotherapy and radiation therapy. The proliferation index is usually calculated visually by professional pathologists who assess the total percentage of positive (labeled) cells. This semi-quantitative counting can be the source of some inter- and intra-observer variability and is time consuming. These factors open up a new field of scientific and technological research and development. Artificial intelligence is attracting attention to solve these problems. Our solution is based on deep learning to calculate the percentage of cells labeled by ki-67 protein. The tumor area with x40 magnification is given by the pathologist to use to segment different types of positive, negative or TIL (tumor infiltrating lymphocytes) cells. The calculation of the percentage comes after the counting of the cells using classical image processing techniques. To give the model our satisfaction, we make a comparison with other datasets of the test and we compare it with the diagnosis of pathologists.

关键词： Ki-67 Proliferation index Deep learning Medical Artificial intelligence Medical imaging Digitization of Ki-67 breast cancer

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Omnifactory: A National Training and Research Testbed for Smart Manufacturing systems 14th

Omnifactory: A National Training and Research Testbed for Sm...

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14th International Conference on Learning Factories (CLF)

作者： Sanderson, David Wang, Zi Bainbridge, David Ratchev, Svetan Univ Nottingham Inst Adv Mfg Nottingham NG7 2GX England

ISBN: (纸本)9783031653995;9783031654008

Omnifactory is a national experimental testbed and technology demonstrator for digital-enabled manufacturing technologies launched in March 2023 that was funded by the UK Industrial Strategy Challenge Fund. Omnifactory has its foundations in a sustained programme of fundamental and applied research at the University of Nottingham, UK, aimed at driving the state-of-the-art and developing technologies for open, adaptable, and cost-effective production systems. It primarily focusses on high-value, high-complexity, low/variable volume production, with a significant contribution to aerostructures assembly, although the technologies are applicable to a wide variety of other industries including automotive, nuclear, space, and defence. By moving away from fixed monolithic production lines and towards a more connected and agile production infrastructure, Omnifactory is enabling industry to leverage modern advanced manufacturing and digital technologies. These approaches can be demonstrated in both the physical world, with a variety of fixed and mobile automation, and in the digital world, with a fully connected digital engineering stack. This paper summarises the industrial and societal trends and drivers that Omnifactory aims to address, as well as its context in the research portfolio at the University;then describes the design, implementation, and use of the facility;outlines some early research, training, and engagement results enabled by it;presents some lessons learned;and finally suggests the outlook of the facility for the future.

关键词： Industry 4.0 Digitalisation Flexible Manufacturing systems Reconfigurable Manufacturing systems Aerospace

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Modelling Stock Prices Prediction with Long Short-Term Memory (LSTM): A Black Box Approach 1

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

作者： Bokhare, Anuja Rao, Madhuri Oliver, M. Pavie Rai, Rohit Adesara, Umang Symbiosis Int Deemed Univ Symbiosis Inst Comp Studies & Res Pune Maharashtra India Dr Vishwanath Karad MIT World Peace Univ Sch Comp Sci Engn & Technol Pune Maharashtra India

ISBN: (数字)9789819984763

ISBN: (纸本)9789819984756;9789819984763

A major requirement for small, medium or large investment firms is to be able to invest in the right stock and attain profitable growth. Giant firms use machine learning intelligence to maintain their competitive edge over the others by trying to predict the next profitable stock precisely. Stock price forecasting is a crucial part of these businesses, and this brings up the research questions such as, what will be the next price of the stock? Does the company need to short the stock? Is long-term holding the best approach at the moment? How to cut losses? Among its many benefits, predictive forecast is a key enabler for a better stock investment model. This research paper introspects on various key aspects of the Long Short-Term Memory (LSTM), a deep learning algorithm which focuses on Recurrent Neural networks (RNNs) and is a black box approach that is apt for predicting stock prices. The paper evaluates the performance of the proposed model and aims to explain the complexity of the system and the need to move towards grey box approach for better comprehension.

关键词： Stock prices prediction Machine learning LSTM Statistical modelling RNN Black box

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Decision-Making Based on Intuitionistic Preference Relations for Emergency Evacuation 1

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Gerasimenko, Evgeniya Bozhenyuk, Alexander Southern Fed Univ Taganrog Russia

ISBN: (数字)9783031700187

ISBN: (纸本)9783031700170;9783031700187

Storms are considered to be one of the most devastating and costliest disasters in the world. According to the disaster data from the International Disaster Database, in a period from 2001 to 2021, about 28% natural hazards are caused by storms. That is why making reasonable decisions regarding evacuation strategy of potential victims is needed. In multi-attribute decision-making, experts may not know the precise values of evaluations. Moreover, it can be hard to predict the degree of preference of one alternative over another so that the certain level of uncertainty exists. Therefore, preference relations in fuzzy conditions are used for these reasons. Intuitionistic fuzzy sets allow handling these uncertain parameters in such a form that considers experts' hesitation regarding assessments of attributes by dealing with membership, non-membership function and indeterminacy degree. In this paper, we consider the necessity of evacuation as the evacuation risky strategy in uncertain conditions as a task of multi-attribute decision-making. A proposed algorithm deals with intuitionistic preference relations to find the desirable evacuation strategy. The proposed algorithm enables transporting the maximum possible number of evacuees to safe destinations allowing intermediate storage. This technique leads to increasing the number of evacuated people. A numerical example is given to justify the results of the algorithm.

关键词： Intuitionistic preference relations intermediate storage dynamic network

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Detecting BGP Routing Anomalies Using Machine Learning: A Review 1

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2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Muosa, Ali Hassan Ali, A. H. Univ Thi Qar Fac Sci Comp & Math Dept Comp Sci Nasiriyah Iraq Univ Kufa Dept Elect & Commun Najaf Iraq

ISBN: (数字)9783031628719

ISBN: (纸本)9783031628702;9783031628719

The Border Gateway Protocol (BGP) is a protocol for exchanging IP prefixes online. It allows for incremental path-vector routing protocol and reachability between Autonomous systems (ASes), enabling efficient global internet activity. BGP anomalies have been known to cause ASes to malfunction in various ways;therefore, detecting them is critical. Machine Learning (ML) solutions currently guarantee improved BGP irregularity recognition based on BGP update messages volume andmanner features, which are frequently boisterous and bursty. This work organizes these anomalies and provides the most recent approaches for detecting discrepancies. We also look into a few crucial requirements for the up-and-coming detection of internet routing abnormalities methods.

关键词： BGP Anomaly Detection Machine Learning Inter-Domain Routing Autonomous systems

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Application of Ensemble Machine Learning for Classification Problems on Very Small Datasets 1

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2nd Serbian International Conference on Applied Artificial Intelligence (SICAAI)

作者： Pavic, Ognjen Dasic, Lazar Geroski, Tijana Pirkovic, Marijana Stanojevic Milovanovic, Aleksandar Filipovic, Nenad Univ Kragujevac Inst Informat Technol Kragujevac 34000 Serbia Univ Kragujevac Fac Engn Kragujevac 34000 Serbia Bioengn Res & Dev Ctr BioIRC Kragujevac 34000 Serbia Univ Kragujevac Fac Med Sci Kragujevac 34000 Serbia

ISBN: (数字)9783031608407

ISBN: (纸本)9783031608391;9783031608407

Machine learning is one of the most widely used branches of artificial intelligence in recent years. It is most commonly used for solving classification or regression problems through the utilization of supervised learning approaches. Machine learning models require high quality and a sufficient quantity of data to produce good results. This paper investigates an approach which incorporates ensemble learning through the aggregation of multiple machine learning models for the purposes of increasing prediction capabilities in cases in which a very limited amount of data is available for training. The ensemble model was trained on a patient fractional flow reserve biomarker dataset and with the goal of classifying patients into risk classes based on their risk of suffering an acute myocardial infarction. The ensemble model was comprised of multiple random forest classification models which were trained with different combinations of training and test data to improve the prediction accuracy over the use of a single random forest model. Final ensemble achieved a prediction accuracy of 71.3% which was an immense improvement over the 36% prediction accuracy of a single random forest classification model.

关键词： Machine learning Classification Risk assessment Random forest Ensemble First Section

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