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Deploying Hybrid VGG19-BiGRU Model for Kidney Disease Segmentation

Deploying Hybrid VGG19-BiGRU Model for Kidney Disease Segmen...

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Intelligent systems Conference

作者： Bappi, Md Basitur Rahman Swapno, S. M. Masfequier Rahman Akhter, Sumiya Rabbi, M. M. Fazle Bangladesh Univ Business & Technol Dept CSE Dhaka 1216 Bangladesh

ISBN: (纸本)9783031663352;9783031663369

The kidney is an essential organ in the human body, and kidney disease is a widespread and sometimes fatal problem. Timely detection of Kidney illness is vital for preventing rapid mortality. Automation in identifying Kidney illness improves its efficacy in facilitating prompt intervention. To tackle this significant issue, it is necessary to implement effective and automated diagnostic solutions. This study focuses on automating the segmentation of Kidney disease. The dataset consists of 12,446 photos categorized into four classes: cyst, normal, stone, and malignancy. We can identify these four diseases using a deep learning technique. We utilize a hybrid VGG19-BiGRu model to segment kidney disease. This model achieves an impressive training accuracy of 99.77% and a validation accuracy of 99.98%. In addition, we incorporate precision, recall, and F1 scores to assess the performance of our model. These results demonstrate that our segmentation criteria are highly effective and positively impact the healthcare system.

关键词： Kidney disease Hybrid model Healthcare system Deep learning Segmentation

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Blockchain Technologies for Transparency in FinTech 9th

Blockchain Technologies for Transparency in FinTech

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9th International Congress on Information and Communication Technology (ICICT)

作者： Bouafia, Khawla Molnar, Balint Majid, Gurbanli Eotvos Lorand Univ Budapest Pazmany Pter Setany 1-C H-1117 Budapest Hungary

ISBN: (纸本)9789819732883;9789819732890

Blockchain technology has the potential to improve transparency in the financial industry, particularly in accounting, auditing, and supply chain management within FinTech. The paper presents a case study of a blockchain-based financial platform, examining its impact on transparency and accountability. It explores how blockchain solutions could enhance transparency in payment, loans, and insurance industries. The paper discusses the implications, advantages, and disadvantages of implementing blockchain-based systems for FinTech transparency, proposing suggestions for further study and legislative changes to support adoption. The goal is to investigate the use of blockchain technology to boost transparency in FinTech, focusing on its advantages and challenges in implementing blockchain for financial processes, including accounting, auditing, and supply chain management.

关键词： Blockchain FinTech Financial services Transparency Cross-border

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The VesselAI Methodology for AI-Powered Decision Support systems for the Maritime Industry 1

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Intelligent systems Conference (IntelliSys)

作者： Kontzinos, Christos Mouzakitis, Spiros Agostinho, Carlos Figueiras, Paulo Askounis, Dimitris Natl Tech Univ Athens Athens 10682 Greece Univ Nova Lisboa Ctr Technol & Syst P-2829516 Caparica Portugal

ISBN: (数字)9783031477218

ISBN: (纸本)9783031477201;9783031477218

The maritime industry is a major contributor in the European economy, employment, and shipping. However, the industry is facing various challenges that concern the need to increase performance and efficiency, optimise costs, obey the strict regulations imposed and increase its overall sustainability when it comes to fuel consumption and other environmental concerns. Such challenges can be resolved with the help of innovative, emerging technologies such as big data, AI, and HPC, the combination of which can lead the way for the development of the next generation of maritime applications. The use of these technologies has the potential to greatly enhance the performance and competitiveness of the maritime industry, but any such initiative must take into account current maritime challenges and needs, as well as the heterogeneity of the maritime environment, processes, and data in order to develop solutions that tackle real problems and correspond to the actual needs of maritime stakeholders. This can only be achieved through cooperation with maritime and other stakeholders and requires comprehensive methodologies that can guide the development of a solution from the early phases of knowledge generation and requirement elicitation to the latter stages of technical development and testing. As such, the current publication presents the VesselAI methodology for AI-powered decision support systems for the maritime industry. VesselAI is an EU-funded project that aims to combine innovative technologies, mainly big data, AI, and HPC to develop the next generation of maritime applications.

关键词： Maritime Methodology Artificial intelligence Decision support Big data

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Using a Human-Like Double Support Force Distribution for Locomotion in Humanoid Robots 27th

Using a Human-Like Double Support Force Distribution for Loc...

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27th International Conference on Climbing and Walking Robots and Mobile Machine Support Technologies (CLAWAR)

作者： Chavez, Francisco Javier Andrade Rajendran, Vidyasagar Mombaur, Katja Univ Waterloo Dept Syst Design Engn CERC Human Centred Robot & Machine Intelligence Waterloo ON Canada Karlsruhe Inst Technol KIT Inst Anthropomat & Robot IAR Optimizat & Biomech Human Centred Robot Karlsruhe Germany

ISBN: (纸本)9783031707216;9783031707223

Locomotion is fundamentally the result of exchanging forces with the environment to generate movement in a desired direction. For bipeds, the most common locomotion is walking. It requires to control the forces applied in the environment to maintain balance and propel the body in the desired direction while respecting friction constraints. The relationship between the center of mass acceleration and contact forces is unique in the single support phase. On the contrary, in the double support phase, it is a non-deterministic problem. It is not uncommon to use heuristics to solve this problem such as minimizing a certain effort criterion. Given how humans regularly solve this problem it is interesting to take inspiration from human walking. The modified Twin Polynomial Method (mTPM) has shown results superior to the state of the art in understanding how humans distribute contact forces in double support [1]. In this paper, we show how human data differs from a 'typical' robot walk and propose using mTPM to generate a more human-like distribution for robots.

关键词： Anthropomorphic robots

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Comparative Analysis of RL-Based Algorithms for Complex systems Control 6th

Comparative Analysis of RL-Based Algorithms for Complex Syst...

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6th International Conference Mechatronics - Ideas for Industrial Applications

作者： Balazy, Patryk Knap, Pawel Podlasek, Szymon AGH Univ Krakow Fac Mech Engn & Robot Al Mickiewicza 30 PL-30059 Krakow Poland

ISBN: (纸本)9783031634437;9783031634444

The increasing complexity and nonlinearityof modern control systems pose serious challenges to achieving effective control. While differential equations have traditionally been the foundation of control theory and optimization with constraints, they may not be sufficient to accurately model chaotic and unbalanced systems. In response to these challenges, newer approaches gaining prominence include the use of policy iteration and Reinforcement Learning (RL). These techniques revolve around the concepts of sequences of actions and rewards, providing a dynamic and adaptable framework for controllers. Learning with reinforcement offers a promising avenue for addressing control theory, enabling systems to robustly adapt to dynamic environments. This paradigm shift away from traditional approaches such as linear-quadratic regulator (LQR) controllers to a more robust reward-based control mechanism is particularly noteworthy. Through a comprehensive analysis of RL-based algorithms, including Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC) and Trust Region Policy Optimization (TRPO), we examine their suitability and performance in crane control scenarios. This study aims to shed light on the advantages and limitations of each algorithm in optimizing crane operations, offering valuable insights for future applications of RL in complex control systems.

关键词： RL control complex nonlinear system agent algorithms analysis

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Exploring Cognitive Load in the Secretary Problem Using EEG Signals

Exploring Cognitive Load in the Secretary Problem Using EEG ...

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Intelligent systems Conference

作者： Mizrahi, Dor Zuckerman, Inon Laufer, Ilan Ariel Univ Dept Ind Engn & Management Ariel Israel

ISBN: (纸本)9783031664274;9783031664281

Human beings often face decision problems involving sequential selections with incomplete information and a trade-off between exploring different options and committing to the current best option. These problems have been called "The Secretary Problem" in Economics, and while their research yielded optimal mathematical solutions, bounded rational agents such as human beings behave sub-optimally in such scenarios. In this work, we seek to explore the problem through the lenses of its neurophysiological markers and contribute new insights to understanding the decision-making process. Specifically, we used an EEG to record the participants' brain patterns while engaged in a "Secretary Problem" task and analyze their brain signals, such as Theta and Beta bands. Our analysis yields two exciting results: (1) the cognitive load associated with the decision-making process varies during the task. In the early and later stages of the selection process, more cognitive resources are being invested than in the middle stages of the experiment. (2) There is a consistent increase in the Theta band power as the number of offers increases, which hints at an increasing need to invest more cognitive resources as the task progresses. These insights have practical implications for the construction of strategic automated agents.

关键词： The secretary problem Decision-making Optimal stopping Theta/Beta ratio

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Intuitionistic Fuzzy Generalized Net Model of Humanoid Robot's Therapeutic Interaction as a Physiotherapy Assistant 1

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Ribagin, Simeon Sotirov, Sotir Sotirova, Evdokia Bulgarian Acad Sci Inst Biophys & Biomed Engn Sofia Bulgaria Univ Prof R Asen Zlatarov Burgas Bulgaria

ISBN: (数字)9783031700187

ISBN: (纸本)9783031700170;9783031700187

In the present study we propose an IFGN1-model of the humanoid robot's therapeutic interaction with a patient during physiotherapy session. The model is based on two mathematical formalisms: the intuitionistic fuzzy sets and the generalized nets. The developed algorithm permits a natural cooperation during the physiotherapy tasks and thus can improve the adherence of the patient and the overall outcomes.

关键词： Humanoid robot interaction Physiotherapy Intuitionistic fuzzy estimation Generalized nets Intuitionistic fuzzy generalized net of first type

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Predictive Analytics and AI-Driven Strategies for Enhanced Cash Flow Forecasting

Predictive Analytics and AI-Driven Strategies for Enhanced C...

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Intelligent systems Conference

作者： Mehra, Shourya Aggarwal, Shivom Mitra, Pritha Quantum Dynam Corp Marseille France

ISBN: (纸本)9783031663284;9783031663291

This paper explores the major link between the accuracy of previous cashflow predictions and the present cashflows, by explaining the strategies of industry factors and consumer segmentation moderate relationship. A latest model is presented to solve these concerns by utilizing the synergistic combination of Long Short-Term Memory (LSTM) with convolutional layers and Random forest. In comparison of simpler models, the prediction accuracy of this architecture is higher because it enables the model to identify both local details and broad trends. The model's flexibility is especially highlighted since it can be adjusted to various datasets and forecasting horizons by utilizing sophisticated feature engineering techniques and modifying critical parameters like network hyperparameters. We encounter the need to avoid common pitfalls in resource allocation and planning, especially those related to causal inference. One potential approach to addressing interesting causal concerns is the recently created reinforcement learning framework. A comprehensive analysis of recent research on reinforcement learning in FP&A establishes the framework for a simulation exercise that shows how machine learning and deep learning both are useful for both planning and forecasting. Furthermore, the study explores how predicting and planning accuracy improves over time as more data points are analyzed, offering important latest information on how financial reinforcement learning is developing.

关键词： Forecast Quarter-months Custom neural network model Random forest model Financial prediction LSTM Reinforcement learning

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Aerodynamic Interaction of Minivan Vehicles During an Overtaking Maneuver

Aerodynamic Interaction of Minivan Vehicles During an Overta...

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International Scientific and Technical Conference on Integrated Computer Technologies in Mechanical Engineering - Synergetic Engineering (ICTM)

作者： Filipkovskij, Sergey Avershyn, Andrii Natl Aerosp Univ Kharkov Aviat Inst UA-61070 Kharkov Ukraine Kharkov Natl Automobile & Highway Univ UA-61002 Kharkov Ukraine

ISBN: (纸本)9783031614149;9783031614156

The minivan has the largest usable volume compared to other passenger car body layouts with the same dimensions. Therefore, it is most advisable to use it in commercial transportation. At the same time, they have the largest lateral surface area, so they experience the greatest wind loads. In the modern era, vehicles are focused on automation to make driving a comfortable experience for the driver. The most important task is control during various maneuvers. Aerodynamic forces significantly affect vehicle control during maneuvers. They must be taken into account when self-driving. The overtaking maneuver of one vehicle by another is a complex, non-stationary process in which complex transient aerodynamic forces arise. The close proximity of two vehicles is characterized by the mutual influence of the aerodynamic structures generated by the vehicles. Improving methods for mathematical modeling of the aerodynamic characteristics of minivan-type vehicles is an actual task and determines the direction of research. The problem of numerical modeling of air flow around vehicles is formulated. A numerical experiment technique has been developed for modeling external flows around vehicles in a non-stationary setting. A study of the flow around vehicles during overtaking made it possible to calculate aerodynamic forces. The dependences of aerodynamic forces on the relative position of vehicles have been established. The lateral aerodynamic forces acting on cars during overtaking are of the same order as the aerodynamic drag forces. These forces must be taken into account in automatic vehicle control systems.

关键词： Minivan Vehicle Overtaking Maneuver Aerodynamic Force Numerical Modeling

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Link Prediction in Stochastic Online Social Network Using Machine Learning Algorithm

Link Prediction in Stochastic Online Social Network Using Ma...

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2024 International Conference on Computing, Communication, Cybersecurity and AI

作者： Rajput, Shivshankar Pimpalapure, Anil Bhanodia, Praveen Sethi, Kamal K. Ansari, Shahnawaz Eklavya Univ Damoh India Acropolis Inst Technol & Res Indore India

ISBN: (纸本)9783031744426;9783031744433

Social networks (SNs) have a huge and diverse impact. Each and every persons are connected with SNs by directly or indirectly. They provide chances for community development, information sharing, and social interaction. But there are drawbacks as well, including as addiction, privacy issues, and the dissemination of false information. Predicting the possibility of a link existing between two nodes in a network is the goal of the network analysis task known as "link prediction." Because graph neural networks (GNNs) can capture intricate relationships inside networks, they have become an effective tool for this endeavour. This research presents a new approach to link prediction based on optimal networks trained using link prediction models as output data and neural networks trained on scale-free networks as input data. A greedy link pruning method is used to address the impact of generalization of neural networks on experiments. The suggested global network structure reliability and network efficiency are taken into account as the goal to fully assess the link prediction performance and benefits of the neural network approach.

关键词： SNs Graph Social Network Link Prediction

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