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arXiv 2024年

作者： Aziz, Abdul Pham, Nhat Vora, Neel Reynolds, Cody Lehnen, Jaime Venkatesh, Pooja Yao, Zhuoran Harvey, Jay Vu, Tam Ding, Kan Nguyen, Phuc College Information and Computer Sciences University of Massachusetts Amherst AmherstMA United States School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science & Engineering University of Texas at Arlington ArlingtonTX United States Department of Neurology The University of Texas Southwestern Medical Center DallasTX United States Earable Inc. BoulderCO United States

Epilepsy is one of the most common neurological diseases globally (around 50M people globally). Fortunately, up to 70% of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The current gold standard, video-EEG (v-EEG), involves attaching over 20 electrodes to the scalp, is costly, requires hospitalization, trained professionals, and is uncomfortable for patients. To address this gap, we developed EarSD, a lightweight and unobtrusive ear-worn system to detect seizure onsets by measuring physiological signals behind the ears. This system can be integrated into earphones, headphones, or hearing aids, providing a convenient solution for continuous monitoring. EarSD is an integrated custom-built sensing-computing-communication ear-worn platform to capture seizure signals, remove the noises caused by motion artifacts and environmental impacts, and stream the collected data wirelessly to the computer/mobile phone nearby. EarSD’s ML algorithm, running on a server, identifies seizure-associated signatures and detects onset events. We evaluated the proposed system in both in-lab and in-hospital experiments at the University of Texas Southwestern Medical Center with epileptic seizure patients, confirming its usability and practicality. © 2024, CC BY-NC-ND.

关键词： Hearing aids

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Fast Network Recovery from Large-Scale Disasters: A Resilient and Self-Organizing RAN Framework

arXiv

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arXiv 2024年

作者： Yagan, M. Yaser Kayraklik, Sefa Kesir, Samed Sumen, Gizem Hokelek, Ibrahim Basaran, Mehmet Alexandropoulos, George C. Basar, Ertugrul Cavdar, Cicek Arslan, Huseyin Gorcin, Ali Lab TÜBİTAK-BİLGEM Kocaeli Turkey The Department of Electronics and Telecommunications Engineering Istanbul Technical University Istanbul Turkey The 6GEN Laboratory Istanbul34854 Turkey The Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece The Department of Electrical and Computer Engineering University of Illinois ChicagoIL60601 United States Department of Electrical and Electronics Engineering Koç University Istanbul Sariyer Turkey The Department of Computer Science KTH Royal Institute of Technology Kista Sweden The Department of Electrical and Electronics Engineering Istanbul Medipol University Istanbul34810 Turkey

Extreme natural phenomena are occurring more frequently everyday in the world, challenging, among others, the infrastructure of communication networks. For instance, the devastating earthquakes in Turkiye in early 2023 showcased that, although communications became an imminent priority, existing mobile communication systems fell short with the operational requirements of harsh disaster environments. In this article, we present a novel framework for robust, resilient, adaptive, and open source sixth generation (6G) radio access networks (Open6GRAN) that can provide uninterrupted communication services in the face of natural disasters and other disruptions. Advanced 6G technologies, such as reconfigurable intelligent surfaces (RISs), cell-free multiple-input-multiple-output, and joint communications and sensing with increasingly heterogeneous deployment, consisting of terrestrial and non-terrestrial nodes, are robustly integrated. We advocate that a key enabler to develop service and management orchestration with fast recovery capabilities will rely on an artificial-intelligence-based radio access network (RAN) controller. To support the emergency use case spanning a larger area, the integration of aerial and space segments with the terrestrial network promises a rapid and reliable response in the case of any disaster. A proof-of-concept that rapidly reconfigures an RIS for performance enhancement under an emergency scenario is presented and discussed. © 2024, CC BY-NC-ND.

关键词： Radio access networks

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Using Arduino Iot Modules As A Low Cost Environmental Research Monitoring System

Using Arduino Iot Modules As A Low Cost Environmental Resear...

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computer Science and Emerging Technologies (CSET), International Conference on

作者： Murad Magableh Ziyad Marie Rajina R. Mohamed Mohd Hafizuddin Bin Ibrahim Julaily Aida Jusoh Rohit Kumar MIS BI and Data Analysis Department Faculty of Business and Financial Science Irbid National University Irbid Jordan Public administration Department Faculty of Administrative sciences and Informatics Al-Istiqlal University Palestine College of Computing dan Informatics Universiti Tenaga Nasional Malaysia Department of Electrical Engineering Politeknik Kuala Terenngganu Kuala Terengganu Malaysia Faculty of Informatics and Computing UniSZA Malaysia School of Computer Science and Engineering Jain Deemed To Be University Bangalore India

Environmental monitoring plays a crucial role in understanding the impact of human activities on our ecosystems. However, the cost and complexity of traditional monitoring systems often hinder widespread deployment, especially in resource-constrained regions. This research paper explores the feasibility and effectiveness of employing Arduino Internet of Things (IoT) modules as a low-cost alternative for environmental research monitoring. The study focuses on designing and implementing an Arduino-based environmental monitoring system capable of gathering real-time data on key environmental parameters, such as temperature, humidity, air quality, and soil moisture. The hardware setup comprises Arduino microcontrollers interfaced with a range of environmental sensors and connectivity modules for data transmission. To assess the system's reliability and accuracy, a series of field experiments were conducted in diverse environmental settings, including urban areas, agricultural zones, and natural habitats. The collected data was compared against measurements from conventional monitoring equipment to validate the Arduino-based system's performance. The research demonstrates that the Arduino IoT modules can effectively monitor and transmit environmental data with a reasonable level of accuracy and stability, making them suitable for various environmental research applications. The low-cost nature of Arduino components and the open-source ecosystem facilitate customization and scalability, enabling researchers and organizations to develop tailored monitoring solutions for specific environmental contexts. Additionally, the paper discusses the challenges and limitations encountered during the deployment of Arduino-based monitoring systems, including power constraints, sensor calibration, and data processing techniques. Furthermore, it explores potential avenues for improvement and optimization to enhance the system's capabilities and data accuracy. In conclusion, this research

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Integrating local and global attention mechanisms for enhanced oral cancer detection and explainability

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computers in Biology and Medicine 2025年 189卷 109841-109841页

作者： Shah, Syed Jawad Hussain Albishri, Ahmed Wang, Rong Lee, Yugyung Computer Science School of Science and Engineering University of Missouri-Kansas City Kansas City United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Oral and Craniofacial Sciences School of Dentistry University of Missouri - Kansas City Kansas City United States

Background and Objective: Early detection of Oral Squamous Cell Carcinoma (OSCC) improves survival rates, but traditional diagnostic methods often produce inconsistent results. This study introduces the Oral Cancer Attention Network (OCANet), a U-Net-based architecture designed to enhance tumor segmentation in hematoxylin and eosin (H&E)-stained images. By integrating local and global attention mechanisms, OCANet captures complex cancerous patterns that existing deep-learning models may overlook. A Large Language Model (LLM) analyzes feature maps and Grad-CAM visualizations to improve interpretability, providing insights into the model's decision-making process. Methods: OCANet incorporates the Channel and Spatial Attention Fusion (CSAF) module, Squeeze-and-Excitation (SE) blocks, Atrous Spatial Pyramid Pooling (ASPP), and residual connections to refine feature extraction and segmentation. The model was evaluated on the Oral Cavity-Derived Cancer (OCDC) and Oral Cancer Annotated (ORCA) datasets and the DigestPath colon tumor dataset to assess generalizability. Performance was measured using accuracy, Dice Similarity Coefficient (DSC), and mean Intersection over Union (mIoU), focusing on class-specific segmentation performance. Results: OCANet outperformed state-of-the-art models across all datasets. On ORCA, it achieved 90.98% accuracy, 86.14% DSC, and 77.10% mIoU. On OCDC, it reached 98.24% accuracy, 94.09% DSC, and 88.84% mIoU. On DigestPath, it demonstrated strong generalization with 84.65% DSC despite limited training data. The model showed superior carcinoma detection performance, distinguishing cancerous from non-cancerous regions with high specificity. Conclusion: OCANet enhances tumor segmentation accuracy and interpretability in histopathological images by integrating advanced attention mechanisms. Combining visual and textual insights, its multimodal explainability framework improves transparency while supporting clinical decision-making. With strong gener

关键词： Image segmentation

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UAV image acquisition and processing for high-throughput phenotyping in agricultural research and breeding programs

Plant Phenome Journal

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Plant Phenome Journal 2024年第1期7卷

作者： Bongomin, Ocident Lamo, Jimmy Guina, Joshua Mugeziaubwa Okello, Collins Ocen, Gilbert Gilibrays Obura, Morish Alibu, Simon Owino, Cynthia Awuor Akwero, Agnes Ojok, Samson National Crops Resources Research Institute (NaCRRI) Kampala Uganda Department of Manufacturing Industrial and Textile Engineering School of Engineering Moi University Eldoret Kenya Department of Information and Communication Technology National Agricultural Research Organisation (NARO) Secretariat Entebbe Uganda Department of Biosystems Engineering Faculty of Agriculture and Environment Gulu University Gulu Uganda Department of Computer Engineering & Informatics Faculty of Engineering Busitema University Tororo Uganda Department of Electrical and Computer Engineering School of Engineering Makerere University Kampala Uganda School of Agricultural Sciences Makerere University Kampala Uganda

We are in a race against time to combat climate change and increase food production by 70% to feed the ever-growing world population, which is expected to double by 2050. Agricultural research plays a vital role in improving crops and livestock through breeding programs and good agricultural practices, enabling sustainable agriculture and food systems. While advanced molecular breeding technologies have been widely adopted, phenotyping as an essential aspect of agricultural research and breeding programs has seen little development in most African institutions and remains a traditional method. However, the concept of high-throughput phenotyping (HTP) has been gaining momentum, particularly in the context of unmanned aerial vehicle (UAV)-based phenotyping. Although research into UAV-based phenotyping is still limited, this paper aimed to provide a comprehensive overview and understanding of the use of UAV platforms and image analytics for HTP in agricultural research and to identify the key challenges and opportunities in this area. The paper discusses field phenotyping concepts, UAV classification and specifications, use cases of UAV-based phenotyping, UAV imaging systems for phenotyping, and image processing and analytics methods. However, more research is required to optimize UAVs’ performance for image data acquisition, as limited studies have focused on the effect of UAVs’ operational parameters on data acquisition. © 2024 The Authors. The Plant Phenome Journal published by Wiley Periodicals LLC on behalf of American Society of Agronomy and Crop Science Society of America.

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Stock Price Forecasting in Finance with Artificial Neural Networks

Stock Price Forecasting in Finance with Artificial Neural Ne...

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Information engineering (ACIE), IEEE Asia Conference on

作者： Turgut Daloğlu Garibe Osi Murat Aslan Mehmet Beşkirli Cizre District Directorate of National Education Şırnak University Şırnak Türkiye Department of Computer Engineering Şırnak University Şırnak Türkiye Departmen of Medical Education and Informatics Faculty of Medicine Karamanoğlu Mehmetbey University Karaman Türkiye

ISBN: (数字)9798331528409

ISBN: (纸本)9798331528416

This paper examines the applications of Artificial Neural Networks (ANNs) in the field of finance, particularly in stock price forecasting. Artificial neural networks are mathematical models that mimic biological nervous systems and are used to solve complex problems such as financial data analysis. Stock price forecasting is an important topic for investors and finance professionals. Artificial neural networks have been widely used in this field due to their ability to process large amounts of financial data, learn complex relationships and predict future price movements. This study deals with stock price forecasting with artificial neural networks using historical stock prices, economic indicators and other financial data. Neural networks attempt to predict future price movements by learning the complex relationships between these data. Stock price forecasting with artificial neural networks can offer a more flexible and adaptive approach than traditional statistical methods. However, the success of the model depends on factors such as the quality of the data set used, network architecture and training parameters. This study was conducted to understand the potential of artificial neural networks in finance and to evaluate their effectiveness in stock price forecasting. The purpose of this study is to explore how artificial neural networks can be used in financial data analysis, and in particular their potential in stock price forecasting. The study is designed to understand the advantages of applying ANN in finance, to evaluate the stock price forecasting performance of this technology and to provide a new perspective that can influence future investment strategies.

关键词： Training Accuracy Statistical analysis Finance Training data Predictive models Data models Forecasting Long short term memory Testing

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Applications of Particle Swarm Optimization Algorithms in Coordination Between Power System Stabilizer and Flexible AC Transmission

Applications of Particle Swarm Optimization Algorithms in Co...

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International Symposium on Intelligent Systems and informatics (SISY)

作者： Majid Moazzami Ghazanfar Shahgholian Zahra Heydaran-Daroogheh-Amnyieh Amirhosein Mosavi Arman Fathollahi Smart Microgrid Research Center Najafabad Branch Islamic Azad University Najafabad Iran Department of Electrical Engineering Najafabad Branch Islamic Azad University Najafabad Iran Department of Computer Engineering Dolatabad Branch Islamic Azad University Dolatabad Iran John von Neumann Faculty of Informatics Obuda University Budapest Hungary Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark

Interconnected power systems require the damping of oscillations to ensure secure operation. Traditional power system stabilizers (PSSs) may, however, not be able to sufficiently dampen inter-area oscillations in multi-machine energy networks. PSSs and flexible AC transmission systems (FACTS) devices are frequently utilized to improve system stability. Meta-heuristic algorithms using search strategies are utilized to find near-optimal solutions. These strategies typically rely on a fitness function to evaluate candidate solutions and are inspired by natural systems. In this work, we furnish a concise overview of particle swarm optimization algorithms applied for the coordinated arrangement of PSS and FACTS controllers, presenting new directions for researchers working on meta-heuristic algorithms and coordination design of FACTS and PSS controllers for inter-area oscillation damping in power systems.

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Research Trends, Challenges, and Future Directions in Vehicular Networks

Research Trends, Challenges, and Future Directions in Vehicu...

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ACS/IEEE International Conference on computer Systems and Applications

作者： Islam Tharwat Abdel Halim Hossam M.A. Fahmy Center for Informatics Science (CIS) School of Information Technology and Computer Science (ITCS) Nile University (NU) Giza Egypt Department of Computers and Systems Engineering Ain Shams University Cairo Egypt

nowadays, vehicular networks constitute the core of the Intelligent Transportation Systems (ITSs) with the main objective is to improve the road safety by taking advantage of the emerging communication technology and the lower cost of wireless devices. Indeed, thanks to sensors installed in vehicles or at the edge of roads and control centers. Vehicle communications will enable drivers to be warned early enough, in case of possible dangers. In addition, these networks are no longer content to improve road safety only, but they also will offer new services to road users making the road more pleasant. The major objective of this article is to provide a brief review of vehicular networks evolution, architecture, characteristics, standards, constraints, and challenges that face designers in the design of protocols dedicated to this type of networks. Also, this paper provides perspectives for future research and developments.

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Trend and Correlation Analysis of Instagram Activity Using Data Mining and Statistics

Trend and Correlation Analysis of Instagram Activity Using D...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Wilem Musu Indra Samsie Aldi Bastiatul Fawait Nadia Lempan Nurliah Maechel Maximiliano Gabriel Sinar Department of Informatics Engineering Dipa Makassar University Makassar Indonesia Department of Information System Dipa Makassar University Makassar Indonesia Department of Computer Science Widya Gama Mahakan University Samarinda Indonesia Department of Digital Business Dipa Makassar University Makassar Indonesia Department of Entrepreneurship Dipa Makassar University Makassar Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

With the growing importance of social media for brand engagement, there remains a limited understanding of how specific metrics-such as Reach, Followers, and New Followers-impact Instagram user interaction over time. This research seeks to address this gap by examining which variables most effectively drive Engagement on Instagram, providing insights for strategic content optimization. Through an analysis of 100 posts, using multiple linear regression and decision tree methods, the study investigates the relationship between Reach, Followers, New Followers, and Engagement. Results indicate that Reach, Followers, and New Followers are not statistically significant predictors of Engagement, as evidenced by high p-values (all> 0.05). Decision tree results demonstrate a Mean Absolute Error (MAE) of 0.0226 and an $\mathrm{R}^{2}$ Score of 0.8003, suggesting moderate predictive accuracy but with potential for refinement. Moving average analysis reveals fluctuating Engagement with no stable long-term trend. A correlation matrix further shows that Reach and New Followers exhibit high correlations with Engagement (0.9723 and 0.9707, respectively), while Followers show a weak correlation (0.2575). These findings highlight the limited impact of Followers on Engagement, underscoring the importance of Reach and New Followers. This study contributes novel insights by identifying key engagement drivers, emphasizing the need for more targeted strategies in Instagram campaign planning to enhance user interaction.

关键词： Correlation Social networking (online) Linear regression Market research Timing Planning Web sites Multimedia communication Decision trees Optimization

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Towards Unified Field-Testing and Monitoring for Safe and Secure Robotic Applications

Towards Unified Field-Testing and Monitoring for Safe and Se...

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Workshop on Robotics Software engineering (RoSE)

作者： Marco Stadler Stefan Biffl Michael Vierhauser Johannes Sametinger LIT SCSL / Inst. of Business Informatics - Software Eng. Johannes Kepler University Linz Austria CDL-SQI Institute of Information Systems Engineering TU Wien and CDP Vienna Austria Department of Computer Science University of Innsbruck Austria

ISBN: (数字)9798331537951

ISBN: (纸本)9798331537968

Problems and failures that emerge in Cyber-Physical Systems (CPSs), particularly in robotic applications, may originate from various sources, including software bugs, security incidents, hardware malfunctions, and human errors. As robotic systems are deployed in various domains and application contexts, such as manufacturing sites, shop floors, agriculture, and autonomous vehicles, ensuring their safe and secure operation is a crucial aspect. While high-fidelity simulations are frequently used to validate system behavior and to perform tests, the “simulator-to-reality gap” presents significant challenges, requiring additional field testing to validate a system under realistic conditions. As simulations alone are insufficient for performing comprehensive testing and for ensuring adherence to both functional and non-functional requirements, real-world field testing helps to alleviate these issues. However, compared to wellestablished unit testing approaches, field testing typically is still a rather ad hoc process, with insufficient support from tools and frameworks. Field tests often heavily rely on human observations, hence risking overlooking critical issues. There is a pressing need for structured, guided field-testing processes combined with adaptive runtime monitoring to capture the data required for effective error diagnosis and analysis. This paper introduces initial concepts for the Smart Unified Runtime Monitoring Infrastructure for Guided Field-Testing (SMURF) framework designed for robotic applications, combining structured test execution with automated, adaptable monitors, to ensure the efficient collection of data required for post-test analysis. Building on prior efforts in drone field-testing frameworks, we extend our scope to identify essential features for testing and monitoring ROS-based systems. Future work shall further refine this process and implement a practical framework to support developers and testers in achieving reliable, safe, and secure

关键词： Runtime Pressing Software Safety Manufacturing Security Reliability Robots Monitoring Testing

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