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A novel U-net model for brain tumor segmentation from MRI images

Alexandria Engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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Autism Spectrum Disorder Detection Via Eye Gaze Analysis

Autism Spectrum Disorder Detection Via Eye Gaze Analysis

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Networking and Communications (ICNWC), International Conference on

作者： Sujay Das Akshat Chotia K. M. Uma Maheswari Sadiq A K Department Of Computer Science & Engineering SRM Institute Of Science & Technology Chennai India College of Computing and Information Science University of Technology and Applied Sciences Oman

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Autism is a complex neuro - developmental condition characterized by challenges in social interaction and communication. Early diagnosis and intervention are crucial for effective support. This paper presents a novel approach for autism detection leveraging Convolutional Neural Networks (CNNs) to analyze eye gaze patterns. The pro0posed method aims to provide an objective and efficient tool for the early screening of Autism. We begin by collecting eye gaze data from a diverse sample of individuals, including both Autism -diagnosed and non Autism individuals. The dataset is preprocessed to extract relevant features from the eye gaze sequences, capturing subtle but significant differences in gaze patterns between the two groups. A CNN architecture is designed and trained on this preprocessed dataset. The network is optimized to automatically learn discriminative features that distinguish between Autism and non Autism gaze patterns. Transfer learning techniques mobilenetv2 are employed to further enhance model performance. The trained CNN demonstrates promising results in accurately classifying individuals into Autism and neurotypical categories. Comprehensive evaluation metrics, including accuracy and loss, attest to the model's effectiveness.

关键词： Measurement Autism Adaptation models Network topology Transfer learning Neural networks Feature extraction

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XAI Based Cattle Identification with YOLO and SIFT Technique

XAI Based Cattle Identification with YOLO and SIFT Technique

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2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Katharria, Aashu Pant, Millie Deep, Kusum Devi, Indu Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Uttarakhand Roorkee India Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand Roorkee India Indian Institute of Technology Roorkee Department of Mathematics Uttarakhand Roorkee India ICAR-National Dairy Research Institute Livestock Production Management Division Haryana Karnal132001 India

ISBN: (纸本)9798350391213

In precision livestock farming, accurate cattle identification is essential for enhancing animal welfare, health monitoring, and productivity, while also supporting traceability and minimizing false insurance claims. This paper presents a novel approach for cattle identification using muzzle prints, with a focus on both efficiency and explainability. Taxicab metric, employed for efficient annotation of muzzle patterns significantly reduces the labeling time for training of YOLOv8 model. YOLOv8 is utilized for detecting muzzle prints in images, followed by SIFT (Scale-Invariant Feature Transform) for feature extraction and matching. The incorporation of Explainable AI (XAI) methods, particularly Grad-CAM, further enhances the transparency and interpretability of the SIFT model. © 2024 IEEE.

关键词： Precision agriculture

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Customer Churn Prediction in the Telecom Sector with Machine Learning and Adaptive k-Means Cluster using Imbalance Data 2

Customer Churn Prediction in the Telecom Sector with Machine...

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2nd International Conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Aravinda, N.L. Reddy, R. Archana Bala Dhandayuthapani, V. Nayak, Jithendra P. R. Husseen, Ahmad Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Engineering Sr University Telangana Warangal India University of Technology and Applied Sciences Shinas Branch Department of It College of Computing and Information Sciences Oman Srinivas Institute of Technology Department of Cse Valachil Mangalore India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

A crucial aspect of maintaining a customer-oriented business in the telecommunications sector with machine learning (ML) is understanding the reasons and factors that lead to customer churn. However, the dataset is difficult by noise, misclassifications, duplicated data, and imbalanced customer information complicating the process of identifying ways to split the dataset based on events. The under-sampling method address the imbalance data by decreasing the data quantity of majority class, thereby achieving a balanced dataset. This method, utilizing Adaptive k-means clustering, directly determines the reduction based on that data quality in each class. The datasets are combined with class labels to generate a new balanced dataset used for classification in the SVM algorithm, which distinguishes between customer churn and non-churn in the prediction for the telecom sector. The SVM algorithm can perform linear and non-linear classification with a kernel function to control overfitting improve generalization and handle high-dimensional data. The obtained results demonstrate that the proposed method achieves better accuracy of 95.70%, precision of 56.01%, Recall of 55.05%, and F1-score of 96.05% on the telecom churn dataset. These results ensure superior detection and classification performance compared to other existing methods, such as Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbour (KNN). © 2024 IEEE.

关键词： Decision trees

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Smart Switch technology: IoT Integration with Touch Control for Improved Accessibility 1

Smart Switch Technology: IoT Integration with Touch Control ...

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1st International Conference on Innovative Engineering Sciences and Technological Research, ICIESTR 2024

作者： Baloch, Farah Khaliq Abro, Ghulam E. Mustafa Alansari, Zainab Hitec University Electrical Engineering Department Taxila Cantt Rawalpindi Pakistan King Fahd University of Petroleum & Minerals Interdisciplinary Research Centre for Aviation and Space Exploration Dhahran Saudi Arabia University of Technology and Applied Sceicnes College of Computing and Information Sciences Muscat Oman

ISBN: (纸本)9798350348637

The Internet of Things (IoT) has ushered in an era of transformative innovation, revolutionizing the way we interact with our surroundings. In the context of home automation, the IoT-Based Smart Switch with Touch Control project represents a leap forward in convenience and efficiency. This research explores the development and implementation of a switch board devoid of mechanical components, relying instead on touch sensors and advanced microcontroller technology. The aim is to create a touch-operated switchboard, of standard size and uncompromising reliability, with the potential to revolutionize everyday living. The research delves into the methodology, key components, beneficiaries, and the constraints involved in this innovative project. Moreover, it provides a detailed account of the project's first evaluation task and the technology used, including the PZEM Power Module, ESP8266 NodeMCU, and Home Assistant. This research illuminates the potential of IoT in simplifying and enhancing the lives of not only the technologically inclined but also senior citizens and the middle class, who may have been previously unable to afford high-cost switch boards with advanced functionality. © 2024 IEEE.

关键词： Microcontrollers

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Career Counselling using AI in the field of IT Industry in Dynamic Environment

Career Counselling using AI in the field of IT Industry in D...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Chandrol, Ayush Awasthi, Monisha Sharma, Deepak Kansal, Mani Sharma, Komal Goel, Ankur Uttaranchal University Uttaranchal School of Computing Sciences Uttarakhand Dehradun India Institute of Applied Medicines and Research UttarPradesh Ghaziabad India Inmantec Institutions UttarPradesh Ghaziabad India KIET Group of Institutions UttarPradesh Ghaziabad India Meerut Institute of Technology UttarPradesh Meerut India

ISBN: (纸本)9798350381887

In the era of lifelong learning, creating easily available services that link employment and educational resources is a challenge for career counselling. There hasn't been much study done on the use of AI to advice in higher education and the workforce thus far. This study explores the potential benefits and enhancements that career counseling at postsecondary educational institutions can bring to the use of artificial intelligence. The outcomes of focus groups, scenario work, and practical trials are mapped to the requirements and potential uses of artificial intelligence in career counseling from the perspectives of students, guidance counselors, and institutions. The findings illustrate both the potential benefits and uses of artificial intelligence in career counseling, along with adoption obstacles and enablers, to enhance postsecondary education and lifelong learning. Drawing on the data, the authors provide many agency and maturity levels for AI integration into guiding processes. Future study should concentrate on agency in advice interactions, expanding the ecology of guidance data, and ethical issues. © 2024 IEEE.

关键词： Professional aspects

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The Role of Machine Learning in Reducing Healthcare Costs: The Impact of Medication Adherence and Preventive Care on Hospitalization Expenses

arXiv

引用

arXiv 2025年

作者： Zhang, Yixin Chen, Yisong The Fu Foundation School of Engineering and Applied Science Columbia University United States College of Computing Georgia Institute of Technology United States

This study reveals the important role of prevention care and medication adherence in reducing hospitalizations. By using a structured dataset of 1,171 patients, four machine learning models Logistic Regression, Gradient Boosting, Random Forest, and Artificial Neural Networks are applied to predict five-year hospitalization risk, with the Gradient Boosting model achieving the highest accuracy of 81.2%. The result demonstrated that patients with high medication adherence and consistent preventive care can reduce 38.3% and 37.7% in hospitalization risk. The finding also suggests that targeted preventive care can have positive Return on Investment (ROI), and therefore ML models can effectively direct personalized interventions and contribute to long-term medical *** Codes 68T05, 68T09, 68U03, 62P10 Copyright © 2025, The Authors. All rights reserved.

关键词： mHealth

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A Machine-Learning Based IoT Smart Home System to Detect and Reduce Urban Insecurity in Uganda: A Case of Kampala Metropolitan Area

A Machine-Learning Based IoT Smart Home System to Detect and...

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International Conference on Advances in Big Data, computing and Data Communication Systems (icABCD)

作者： Edward Miiro Ismael Kato Department of Computer Science & Engineering Faculty of Computing & Informatics Makerere University Business School Kampala Uganda Dept. of Applied Computing & Information Technology Faculty of Computing & Informatics Makerere University Business School Kampala Uganda

ISBN: (数字)9798350387902

ISBN: (纸本)9798350387919

This study sought to develop and test a machine learning based prototype of a smart home security system for Kampala Metropolitan area in Uganda. The researchers used a Design Science Research (DSR) method to execute the project. The researchers identified a problem- which was high level of insecurity and crimes around homes in Kampala and its metropolitan areas, defined objectives, collected requirements, designed and developed the artifact, then demonstrated, evaluated, and communicated the artifact to stakeholders. The team created a working prototype of a home-security system that was later tested, demonstrated, validated among potential users. Practically, this project was implemented in a simulated environment using Tinkercad Arduino software. The developed system prototype was simulated on screen and tested on at least one home to ensure its effectiveness in reducing unauthorized entry, robbery, and other forms of crimes that are widely prevalent in Kampala today. One limitation of the project was that Arduino board used was quite limited on the number of pins that were available for use. The initial project idea had featured a temperature sensor to that was intended to regulate the temperature in the home by making use of the fan. However, the researchers did not implement this due to the limited pins and the capacity of the Arduino Uno R3 board that was used. Higher capacity industrial tools could provide a more realistic design of a similar system. The project simulated an environment of a smart home security system that can be used to detect and reduce crime. This study is part of a wider project to test ML and IoT systems in building secure smart homes. The authors envisage the application on more advanced ML algorithms and techniques in improving the system.

关键词： Temperature sensors Urban areas Prototypes Smart homes Machine learning Software Pins

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Foot Augmentation 101: Design your own Augmented Experiences 24

Foot Augmentation 101: Design your own Augmented Experiences

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18th International Conference on Tangible, Embedded, and Embodied Interaction, TEI 2024

作者： Wittchen, Dennis Sabnis, Nihar Nachtigall, Troy Robert Mueller, Florian Floy Strohmeier, Paul Elvitigala, Don Samitha University of Applied Sciences Dresden Germany Sensorimotor Interaction Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Eindhoven University of Technology Eindhoven Netherlands Exertion Games Lab Department of Human Centred Computing Monash University Melbourne Australia

ISBN: (纸本)9798400704024

This studio aims to collaboratively build foot augmentations, experiment with different materials and techniques, and create new designs for low-cost, wearable, and accessible devices that can be used by researchers, makers, designers, and artists. Considering the heightened focus on the human body with the rise of AR/VR/XR technologies, foot augmentation has great potential. To explore this potential, we invite researchers, designers and artists to share their applications, experiences, and ideas while designing foot augmentations. Participants will share knowledge, brainstorm ideas, and explore tools and materials for rapid prototyping. Finally, they will tinker and explore, discussing their design strategies to derive common approaches and best practices. Based on the hands-on session results, we will write a paper on design strategies for foot augmentation that will help facilitate more sustainable investigations and design of future foot interfaces. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： augmenting experiences foot augmentation foot interaction

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Joint Multiple Efficient Neighbors and Graph Learning for Multi-view Clustering 1

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10th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2023

作者： Sadjadi, Fatemeh Torra, Vicenç Department of Applied Mathematics Faculty of Sciences and Modern Technologies Graduate University of Advanced Technology Kerman117-76315 Iran Department of Computing Science MIT Building Umeå University Västerbotten Umeå901 87 Sweden

ISBN: (数字)9783031467813

ISBN: (纸本)9783031467806

Graph-based multi-view clustering is a popular method for identifying informative graphs for e.g. computer vision applications. Nevertheless, optimizing sparsity and connectivity simultaneously is challenging. Multi-view clustering (MC) integrates complementary information from different views. However, most existing methods introduce noise or ignore relevant data structures. This paper introduces a Joint multiple efficient neighbors and Graph (JMEG) learning method for MC. Our approach includes a post-processing technique to optimize sparsity and connectivity by means of identifying neighbors efficiently. JMEG also uses partition space and consensus graph learning to uncover data structures efficiently. Experiments show that JMEG outperforms state-of-the-art methods with negligible additional computation cost. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Data structures

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