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Brain tumor automated diagnosis from MR images via attention-guided semi-supervised deep learning 3

Brain tumor automated diagnosis from MR images via attention...

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3rd international conference on Optics and Machine Vision, ICOMV 2024

作者： Awudong, Buhailiqiemu Li, Qi School of Computer Science and Technology Changchun University of Science and Technology Jilin Changchun China Zhongshan Institute of Changchun University of Science and Technology Guangdong Zhongshan China

ISBN: (纸本)9781510680319

Brain tumors can be life-threatening. Early detection and accurate determination of the type and location of brain tumors are crucial for intervening in the condition of brain tumor patients and saving lives. In areas with limited medical facilities and doctor resources, obtaining timely diagnosis from medical experts can be challenging, resulting in missed treatment opportunities. The use of the medical Internet of Things for remote diagnosis of brain tumors, which includes automatic diagnosis, is a solution to the global issue of imbalanced distribution of equipment and expert resources. Therefore, it is crucial to develop a robust and highly accurate intelligent diagnosis system for brain tumors. Acquiring and annotating brain tumor MR image data is a time-consuming and expensive process due to the large image sizes and limited sample annotation data available. These factors pose significant challenges to the robustness and accuracy of the model. To address these issues, we propose a new deep semi-supervised learning approach that fully utilizes unlabeled sample data. data augmentation methods were used in the model to increase training data, avoid overfitting, and improve the model's generalization ability. A new sliding window feature extraction method was used to avoid resizing images, which may lead to loss and neglect of small features, in order to accurately diagnose brain tissue lesions of small brain tumors that are difficult to recognize. The feature extraction backbone network introduced the Convolutional Block Attention Module (CBAM) attention mechanism, which enabled the network to fully understand image information in both spatial and channel aspects, enhancing the network's perception ability of key features. Experiments were conducted on publicly available brain tumor datasets to validate the advantages of our method. © 2024 SPIE.

关键词： Feature extraction

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Multi-Agent Systems for Autonomous IoT Network Management Using Distributed Reinforcement learning 3

Multi-Agent Systems for Autonomous IoT Network Management Us...

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3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Neelamegam, G. Venkatesan, Rajaram Ramya, S.R. Ramya, R.S. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak VSB College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Builders Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tirupur India Dr NGP Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems. © 2025 IEEE.

关键词： Resource allocation

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EduBot: A Proof-of-Concept for a High School Motivational Agent 23rd

EduBot: A Proof-of-Concept for a High School Motivational Ag...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Faria, Hugo Barbosa, Maria Araujo Veloso, Bruno Marcondes, Francisco S. Lima, Celso Duraes, Dalila Novais, Paulo Univ Minho ALGORITMI Ctr Braga Portugal High Sch Caldas Taipas Caldas Das Taipas Guimaraes Portugal

ISBN: (纸本)9783031217524;9783031217531

Motivation appears to plays a role in student dropout. This paper uses a dataset with high school information (756 entries) for creating a model that relates year failures with the other features on the dataset. The resulting model, based on XGBoost, shows that the top-ranked features are related with motivation issues corroborating with the initial observation. In this sense, a digital assistant embedded with a motivation module may aid on improving motivation and on avoiding dropout. In other words, if the predictor detects year failure possibility it start to act on motivating the student. Considering this paper being direct to underage students, this paper stops on the proof-of-concept level considering the actual dialogues and live tests are expected to performed with the support of educational psychologists.

关键词： Educational data mining Motivational assistant Predict at-risk students Student's performance

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ICU Mortality Prediction Using Long Short-Term Memory Networks 23rd

ICU Mortality Prediction Using Long Short-Term Memory Networ...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Mili, Manel Kerkeni, Asma Ben Abdallah, Asma Bedoui, Mohamed Hedi Univ Monastir Fac Med Monastir Tunisia Univ Monastir Higher Inst Comp Sci & Math Monastir Tunisia Univ Monastir Lab Technol & Med Imaging Fac Med Monastir Tunisia

ISBN: (纸本)9783031217524;9783031217531

Extensive bedside monitoring in Intensive Care Units (ICUs) has resulted in complex temporal data regarding patient physiology, which presents an upscale context for clinical data analysis. In the other hand, identifying the time-series patterns within these data may provide a high aptitude to predict clinical events. Hence, we investigate, during this work, the implementation of an automatic data-driven system, which analyzes large amounts of multivariate temporal data derived from Electronic Health Records (EHRs), and extracts high-level information so as to predict in-hospital mortality and Length of Stay (LOS) early. Practically, we investigate the applicability of LSTM network by reducing the time-frame to 6-hour so as to enhance clinical tasks. The experimental results highlight the efficiency of LSTM model with rigorous multivariate time-series measurements for building real-world prediction engines.

关键词： Electronic health record Multivariate time-series data MIMIC-III

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Explainable Artificial Intelligence for Improved Modeling of Processes 23rd

Explainable Artificial Intelligence for Improved Modeling of...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Velioglu, Riza Goepfert, Jan Philip Artelt, Andre Hammer, Barbara Bielefeld Univ CITEC Bielefeld Germany Recommendy UG Bielefeld Germany

ISBN: (纸本)9783031217524;9783031217531

In modern business processes, the amount of data collected has increased substantially in recent years. Because this data can potentially yield valuable insights, automated knowledge extraction based on process mining has been proposed, among other techniques, to provide users with intuitive access to the information contained therein. At present, the majority of technologies aim to reconstruct explicit business process models. These are directly interpretable but limited concerning the integration of diverse and real-valued information sources. On the other hand, Machine learning (ML) benefits from the vast amount of data available and can deal with high-dimensional sources, yet it has rarely been applied to being used in processes. In this contribution, we evaluate the capability of modern Transformer architectures as well as more classical ML technologies of modeling process regularities, as can be quantitatively evaluated by their prediction capability. In addition, we demonstrate the capability of attentional properties and feature relevance determination by highlighting features that are crucial to the processes' predictive abilities. We demonstrate the efficacy of our approach using five benchmark datasets and show that the ML models are capable of predicting critical outcomes and that the attention mechanisms or XAI components offer new insights into the underlying processes.

关键词： Machine learning Process mining Transformer XAI

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Endowing intelligent Vehicles with the Ability to Learn User's Habits and Preferences with Machine learning Methods 23rd

Endowing Intelligent Vehicles with the Ability to Learn User...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Barbosa, Paulo Ferreira, Flora Fernandes, Carlos Erlhagen, Wolfram Guimaraes, Pedro Wojtak, Weronika Monteiro, Sergio Bicho, Estela Univ Minho Res Ctr Algoritmi P-4800058 Guimaraes Portugal Univ Minho Res Ctr Math P-4800058 Guimaraes Portugal

ISBN: (纸本)9783031217524;9783031217531

A private vehicle frequently carries the same passengers who routinely take specific objects with them, have their vehicle comfort preferences, and visit the same places at relatively the same time of a given day or day of the week. Thus, developing intelligent vehicles that are able to reduce the cognitive workload of the drivers by learning and adapting to their occupants' routines is of the highest interest. In this paper, we present two independent models based on machine learning methods, including artificial neural networks and linear and ridge regressions, to learn the habits and preferences of the vehicle's users. The first model is responsible for predicting the next vehicle trip state, i.e., the departure location and time, and the driver, passenger, and object states. The second model anticipates the comfort setting inside the cockpit - temperature, cockpit mirror, and driver seat poses. The developed models were trained, evaluated, and validated with different datasets in the Portuguese city of Braga. The results prove that the vehicle efficiently learns the routines of several users with varying complexities. Prediction errors happen in cases of an exceptional, one-time deviation from routine behavior.

关键词： learning driver habits Human mobility patterns Time and space prediction Deep learning intelligent vehicles

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The Covid-19 Influence on the Desire to Stay at Home: A Big data Architecture 23rd

The Covid-19 Influence on the Desire to Stay at Home: A Big ...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Sousa, Regina Oliveira, Daniela Carneiro, Ana Pinto, Luis Pereira, Ana Peixoto, Ana Peixoto, Hugo Machado, Jose Univ Minho LASI ALGORITMI Res Ctr Braga Portugal

ISBN: (纸本)9783031217524;9783031217531

The COVID-19 pandemic has had an impact on many aspects of society in recent years. The ever-increasing number of daily cases and deaths makes people apprehensive about leaving their homes without a mask or going to crowded places for fear of becoming infected, especially when vaccination was not available. People were expected to respect confinement rules and have their public events cancelled as more restrictions were imposed. As a result of the pandemic's insecurity and instability, people became more at ease at home, increasing their desire to stay at home. The present research focuses on studying the impact of the COVID-19 pandemic on the desire to stay at home and which metrics have a greater influence on this topic, using Big data tools. It was possible to understand how the number of new cases and deaths influenced the desire to stay at home, as well as how the increase in vaccinations influenced it. Moreover, investigated how gatherings and confinement restrictions affected people's desire to stay at home.

关键词： Big data Business intelligence COVID-19 Stay at home Behavior analysis

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Robotic Scheduling Strategies based on Machine learning Algorithm 3

Robotic Scheduling Strategies based on Machine Learning Algo...

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2023 3rd international conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2023

作者： Wu, Hongzhong Xing, Tenghui Li, Weiqing Chen, Hongbiao Kang, Yutao Li, Jiayang School of Electronic Information and Electrical Engineering Huizhou University Huizhou China College of Big Data and Internet Shenzhen Technology University Shenzhen China

Recently, robot scheduling has been widely concerned by numerous companies and researchers. They adopt an intelligent algorithm to design scheduling strategies for automated guided vehicle (AGV) and achieve required moving routes and certain aims. However, the existing robot scheduling strategies employ mathematical program models to arrange robots' control and ignore machine issues that the robots may have. This will cause conflict and may lead the whole robot to a deadlock when the scale of conflict reaches a critical point. In this paper, we present a novel scheduling strategy for intelligent robots by training machine learning algorithms. Initially, we assume the robots contain a communication system, which can only receive signals from the central controller. Subsequently, we utilize the input parameters including robot conditions, working area, and working aims to train the machine learning algorithm. The work accomplishment ratio and conflict ratio are two essential indicators to measure our proposed method. Additionally, we also simulate the existing robot scheduling models to compare. From the extensive experimental results and comparison evaluations, we can conclude that our proposed method can achieve the robots scheduling tasks with reasonable costs. At last, we discuss the challenges of applying machine learning techniques to robotic scheduling and potential solutions and provide future directions in this field. © Published under licence by IOP Publishing Ltd.

关键词： intelligent robots

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A Comparative Study of LAD, CNN and DNN for Detecting Intrusions 23rd

A Comparative Study of LAD, CNN and DNN for Detecting Intrus...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Chauhan, Sneha Mahmoud, Loreen Gangopadhyay, Sugata Gangopadhyay, Aditi Kar IIT Roorkee Dept Comp Sci & Engn Roorkee Uttar Pradesh India NIT Uttarakhand Dept Comp Sci & Engn Srinagar India IIT Roorkee Dept Math Roorkee Uttar Pradesh India

ISBN: (纸本)9783031217524;9783031217531

In recent years, with the growth of the Internet and network devices, a significant amount of information has been exposed to the attackers and intruders. Due to vulnerabilities in the system, the adversaries plan new ways of network intrusions. Many Intrusion Detection Systems (IDSs) are developed to protect the networks from malicious attacks and ensure reliability and availability within the organizations. IDSs built using various machine learning and data mining techniques are effective in detecting attacks. However, their performance decreases with an increase in the size of data. In this paper, we focus on developing an IDS model using Logical Analysis of data (LAD). It is a supervised learning technique where patterns are generated using partially defined Boolean functions (pdBf), which can detect attacks based on certain features of the data. We compare the performance of LAD model with Deep Neural Network (DNN) and Convolutional Neural Network (CNN) IDS models. UNSW-NB15 and CSE-CIC-IDS2018 datasets are used for training and testing our proposed model. The results show that the performance of LAD model is competitive to CNN, DNN and other existing IDS models based on accuracy, precision, recall and F1 score.

关键词： Logical analysis of data Intrusion detection system Deep neural network Convolutional neural network

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Demand Forecasting for Market Basket Analysis 3rd

Demand Forecasting for Market Basket Analysis

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3rd international conference on Emerging Electronics and Automation, E2A 2023

作者： Alex, S. Satish, K. Anitha, V. Kalaiselvi, S. Santhi, S. Department of IT National Engineering College Tamil Nadu Kovilpatti India Department of Computer Science National Engineering College Tamil Nadu Kovilpatti India

ISBN: (纸本)9789819768011

Demand forecasting supports the business to arrive at more intelligent supply decisions by estimating future sales and revenue. By anticipating future sales, it enables organizations to optimize inventories. Demand managers may make well-informed company decisions about all that from planning for supplies and warehousing requirements to running quick sales and satisfying consumer expectations by examining past sales data. The primary objective is to build a model using light gradient boosting machine regression that precisely predicts sales for the upcoming year using machine learning. This model renders great interpretability and high efficiency, making it ideal for use in industrial production environments. In order to estimate sales for the upcoming 12 months for 50 distinct items across ten different stores, sales data for retail items from the last five years have been gathered. The model utilizes past transaction data to forecast upcoming daily sales. The data comes with an upward trend and certainly seems seasonal. The assumption is further supported by the fact that the mean or average of the residuals appears to be zero. A continuous forecast is updated periodically throughout the year, which predicts a company's performance over an ongoing period of time. Date-time features are properties that are derived from the time stamp value of each observation. The data have been split into both the train and the test sets, in which the train set including sales data from the past five years and the test set including sales from the last twelve months. For the performance measurement of errors, the following metrics are evaluated: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R2-squared using grid search model and have validated our cross-validation score. The information from this analysis has given the vendor the ability to foresee and estimate future demand from customers for a specific service or product. © The Author(s), under exclusive license

关键词： Sales

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