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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Haas, Sam Hartman, Timothy W. Gurung, Bichar Dip Shrestha Do, Tuyen Sani, Rajesh Gadhamshetty, Venkataramana Gnimpieba, Etienne University of South Dakota Dept. of Biomedical Engineering Sioux Falls United States University of South Dakota Dept. of Computer Science Vermillion United States South Dakota School of Mines and Technology Dept. of Chemical and Biological Engineering Rapid City United States Dept. of Civil and Environmental Engineering South Dakota School of Mines and Technology Rapid City United States

ISBN: (纸本)9781665468190

Biological and biomedical microscope image (bioimage) comparison remains useful to approach many research challenges - from biofilms to human diseases. This powerful technology allows researchers to provide the community with a quick visual snapshot of varying experimental conditions. But a two-condition comparison still relies on a researcher's eyes to draw conclusions despite the availability of multiple - often complex - digital image analysis tools. Our Bioimage Analysis, Statistic, and Comparison (BASIN) software provides an easy, objective, reproducible comparison leveraging inferential statistics to bridge image data analysis with other biomedical data modalities such as gene expression. Users have access to a machine learning module to assist with image segmentation using modern, trainable algorithms. BASIN also provides several key data points including images' object counts, net and mean pixel intensities, net and mean object surface areas, plus a variety of other potentially useful data. Hypothesis testing is performed on mean object intensities and surface areas using the statistical power of the R programming language. These features allow BASIN to extend the current scope of image comparison. It gives researchers a multi-model knowledge about matters such as drug protein marker response, the significance of cell population changes, and changes in cell morphology. To improve BASIN's accessibility and transparency we implemented it in R using Shiny framework and provided both an online trial version and a customizable offline version. We also have a batch version to run on datasets with hundreds of biomedical images. BASIN workflows consist of five core modules including image upload, feature extraction, statistical analysis, visualization, and report generation. © 2022 IEEE.

关键词： Machine learning

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Unlearning Climate Misinformation in Large Language Models

arXiv

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arXiv 2024年

作者： Fore, Michael Singh, Simranjit Lee, Chaehong Pandey, Amritanshu Anastasopoulos, Antonios Stamoulis, Dimitrios Microsoft Corporation United States Dept. of Electrical and Biomedical Engineering University of Vermont United States Dept. of Computer Science George Mason University United States Archimedes AI Unit RC Athena Athens Greece

Misinformation regarding climate change is a key roadblock in addressing one of the most serious threats to humanity. This paper investigates factual accuracy in large language models (LLMs) regarding climate information. Using true/false labeled Q&A data for finetuning and evaluating LLMs on climate-related claims, we compare open-source models, assessing their ability to generate truthful responses to climate change questions. We investigate the detectability of models intentionally poisoned with false climate information, finding that such poisoning may not affect the accuracy of a model’s responses in other domains. Furthermore, we compare the effectiveness of unlearning algorithms, fine-tuning, and Retrieval-Augmented Generation (RAG) for factually grounding LLMs on climate change topics. Our evaluation reveals that unlearning algorithms can be effective for nuanced conceptual claims, despite previous findings suggesting their inefficacy in privacy contexts. These insights aim to guide the development of more factually reliable LLMs and highlight the need for additional work to secure LLMs against misinformation attacks.1 © 2024, CC BY.

关键词： Climate change

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Video Streaming QoS Analysis with TIPHON Standard NDNts 9

Video Streaming QoS Analysis with TIPHON Standard NDNts

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： J.d, I Made Aditya Putra Alifa, Lisdatul Fawwaz, Muhammad Hilmi Mauren Sibayak, Nissha Kinaya Mayasari, Ratna Arifin, Hasan Nur Ahdan, Syaiful Negara, Ridha Muldina Syambas, Nana Rachmana Bandung Institute of Technology Dept. of Telecommunication Engineering Bandung40132 Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia

ISBN: (纸本)9798350305029

Named data networks are a new concept in network architecture that can process data transmission by name. This concept can be utilized in the case of video streaming. TIPHON is a standard used to support streaming multimedia activities. According to TIPHON, three factors-delay, packet loss, and throughput-can influence how a stream behaves. This study's goal is to evaluate the quality of service (QoS) of video streaming services using an NDN network that adheres to the TIPHON NDN standard. Several things can affect throughput, such as bottlenecks on the computer or network device used. © 2023 IEEE.

关键词： Quality control

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Machine Learning Approaches for Early Diagnosis of Breast Cancer: A Comparative Study of Performance Evaluation

Machine Learning Approaches for Early Diagnosis of Breast Ca...

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IEEE International Conference on Electro-Information Technology

作者： Rabia Al Mamlook Sujeet Shresth Tasnim Gharaibeh Ali Saeed Almuflih Wassnaa Al-Mawee Hanin Bzizi Dept. Industrial Engineering University of Al-Zawiya Al-Zawiya Libya Trine University Indian USA Dept. Industrial Engineering Wayne Stata University MI USA Dept. of computer science Kalamazoo College Kalamazoo USA Dept. Industrial Engineering King Khalid University Guraiger Saudi Arabia Dept. of Computer Science Department of Computer Science Western Michigan University Kalamazoo MI Department of Biomedical Science Western Michigan University Kalamazoo MI

Breast cancer is a leading cause of death among women worldwide. Early detection and diagnosis are crucial to improving the chances of survival. This paper presents a study on the diagnosis of breast cancer using various machine-learning approaches. The study includes the performance evaluation of nine different techniques using confusion matrix accuracy for Sensitivity, Specificity, Precision, PME, PPV, NPV, and Model Accuracy. AdaBoost is found to have the highest Sensitivity and PME, while Random Forest and MLP gave the best Specificity and Precision. Logistic Regression is found to be the best model for accuracy with 97.8%, followed by SVM with 96.49%, Random Forest with 95.61%, and KNN & Decision Forest with 94.73%. The proposed approach is found to have the highest accuracy of 97.80 % compared to other approaches studied. Our results indicate that the proposed approach using discretization can significantly improve the signal-to-noise ratio in the diagnosis of breast cancer. This approach can accurately predict and diagnose breast cancer using a subset of features.

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Brain Tumor Detection using Convolutional Neural Networks (CNNs)

Brain Tumor Detection using Convolutional Neural Networks (C...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Sasimekala M M Siva V Shanmuga Shyam B Veeramalai Sankaradass C Selvaganesan R. Ponnusamy Department of Biomedical Engineering Chennai Institute of Technology Chennai Tamil Nadu India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The brain's unchecked and fast cell development fuels a tumor. It may prove lethal if left untreated in the early stages. Accurate segmentation and classification remain difficult despite many noteworthy efforts and promising results. The differences in the tumor size, form, and proportions make brain tumor detection extremely difficult. The primary goal of this study is to provide researchers with a thorough literature review on the use of magnetic resonance (MR) imaging in diagnosing brain malignancies. This study suggested multiple methods for detecting tumors and brain cancer using statistical image processing and artificial intelligence. An assessment matrix for a certain system employing particular systems and dataset kinds is also displayed in this study. The anatomy of brain tumors, available data sets, augmentation techniques, component extraction, and classification between machine learning (ML) and deep learning (DL) models are also explained in this work. Lastly, the study gathers all pertinent information for identifying and comprehending tumors, including their advantages, disadvantages, developments, and future trends. In this study, an attempt is made to classify the tumors using CNNs.

关键词： Deep learning Image segmentation Magnetic resonance Imaging Market research Data models Fuels Data mining Systematic literature review Image classification

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ULung:A Novel Approach for Lung Image Segmentation

ULung:A Novel Approach for Lung Image Segmentation

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Computing and informatics (ICCI), International Conference on

作者： Osim Kumar Pal Shuvasish Roy Anup Kumar Modok Tajmihir Islam Teethi Siddhartha Kumer Sarker Dept. of Biomedical Engineering University of Kragujevac Kragujevac Serbia Dept. of Electrical & Electronic Engineering American International University-Bangladesh Dhaka Bangladesh Dept. of Computer Science & Engineering Daffodil International University Ashulia Savar Bangladesh Dept. of Space Communication & Navigation Technology BSMRAAU Old Airport Dhaka Bangladesh

ULung is an innovative medical technology used for segmenting medical images in pulmonary diagnostics. It introduces a modified approach to analyzing and understanding pulmonary conditions. ULung employs cutting-edge deep learning and image processing techniques to isolate intricate characteristics in medical lung images properly. The model uses advanced convolutional neural network architecture as UNet layer stricture to detect abnormalities and delineate anatomical regions reliably. ULung exhibits versatility in handling various lung ailments after extensive training using diverse datasets. The innovative methodology ensures enhanced segmentation performance, enhancing diagnostic precision and expediting medical evaluations. The advent of ULung has greatly enhanced medical imaging, providing clinicians with a potent tool for precise and expeditious lung evaluation. ULung is an impressive advancement in medical image segmentation that can revolutionize standards in respiratory healthcare due to its robustness and adaptability. The model's accuracy achieved using EfficientNetV2 is 99%, while its precision and recall rates are 98% and 96%, respectively. The model achieves a Mean Intersection over Union (MeanIoU) of 91.48% after the 14th epoch.

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CRISP - DM Process in Smart Building Management

CRISP - DM Process in Smart Building Management

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International Conf on Electrical and Electronic Engineering (ICEEE)

作者： Mahir Dursun Moldir Sabyrzhan Dept. of Electrical and Electronics Engineering Faculty of Technology Gazi University Ankara Türkiye Department of Computer Science Graduate School of Informatics Gazi University Ankara Türkiye

ISBN: (数字)9798350362541

ISBN: (纸本)9798350362558

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM).

关键词： Heating systems Smart buildings Laboratories Energy conservation Decision making Process control Data models Planning Internet of Things Data mining

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Towards Personalized Fitness with Multi-modal Wearable Heart Rate Monitors: A Proposal

Towards Personalized Fitness with Multi-modal Wearable Heart...

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Vocational Education and Electrical Engineering, International Conference on

作者： Alfian Dwi Nugraha Pradini Puspitaningayu Rahmat Hidayatullah Eko Sakti Pramukantoro Parama Diptya Widayaka Naim Rochmawati Dept. of Informatics Engineering State University of Surabaya Surabaya Indonesia Dept. of Electrical Engineering State University of Surabaya Surabaya Indonesia Faculty of Computer Science Brawijaya University Malang Indonesia

Nowadays, the trend for home workouts is getting more and more popular. The growth of wearable fitness trackers from basic accelerometers, pedometers, pulse detectors, and ECGs also supports the advancement of personal fitness monitoring. Everyone has a distinct level of fitness, in theory. The heart rate, which is correlated with physical activity, such as sports, demonstrates how this organ functions. As the muscles work harder at a high intensity, the heart rate will also rise. This paper presents the design and implementation of a multimodal wearable heart rate monitoring system. The software is Android-based and is designed to be able to receive heart rate data sent by two different wearable heart rate monitors simultaneously using Bluetooth low-energy protocol. One uses an optical heart monitor and the other uses an electrocardiogram or ECG. The implementation also includes IoT for real-time data acquisition and a web-based application for wider accessibility. The system is evaluated by collecting packets from two devices for 60 minutes. The results underscore the feasibility of the system in collecting users’ heart rate data either using different devices. Furthermore, it serves as a foundation for future studies aiming to expand the personalization capabilities of wearable devices in healthcare and fitness.

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Accelerating Privacy-Preserving Medical Record Linkage: A Three-Party MPC Approach

arXiv

引用

arXiv 2024年

作者： Magara, Şeyma Selcan Dietrich, Noah Ünal, Ali Burak Akgün, Mete Medical Data Privacy and Privacy-Preserving ML on Healthcare Data Dept. of Computer Science University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen Germany

Record linkage is a crucial concept for integrating data from multiple sources, particularly when datasets lack exact identifiers, and it has diverse applications in real-world data analysis. Privacy-Preserving Record Linkage (PPRL) ensures this integration occurs securely, protecting sensitive information from unauthorized access. This is especially important in sectors such as healthcare, where datasets include private identity information (IDAT) governed by strict privacy laws. However, maintaining both privacy and efficiency in large-scale record linkage poses significant challenges. Consequently, researchers must develop advanced methods to protect data privacy while optimizing processing performance. This paper presents a novel and efficient PPRL method based on a secure 3-party computation (MPC) framework. Our approach allows multiple parties to compute linkage results without exposing their private inputs and significantly improves the speed of linkage process compared to existing privacy-preserving solutions. We demonstrated that our method preserves the linkage quality of the state-of-the-art PPRL method while achieving up to 14 times faster performance. For example, linking a record against a database of 10,000 records takes just 8.74 seconds in a realistic network with 700 Mbps bandwidth and 60 ms latency. Even on a slower internet connection with 100 Mbps bandwidth and 60 ms latency, the linkage completes in 28 seconds, highlighting the scalability and efficiency of our solution. © 2024, CC BY.

关键词： Differential privacy

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An Expressive Model for the Specification and Analysis of Obligations

SSRN

引用

SSRN 2023年

作者： Fernández, Maribel Alves, Sandra Dept. of Computer Science University of Porto Porto Portugal King’s College London Dept. of Informatics LondonWC2R 2LS United Kingdom

The notion of obligation is used in a variety of domains, ranging from legal frameworks to cybersecurity (e.g., in access control policies, to specify privacy guarantees in web services and mobile applications, to deal with data collection constraints in the Internet of Things). We define an axiomatic model of obligations as an extension of the category-based access control (CBAC) metamodel. An abstract notion of event is used to identify system states that trigger obligations or fulfill them. The category-based model of obligations inherits the generality of the CBAC metamodel, allowing us to specify obligations in all of the abovementioned domains. Obligations can be defined on their own or linked to data collection or access control decisions. In the latter case, the access control policy should be compatible with the obligation policy to ensure that obligations can be fulfilled. We provide techniques to analyse obligations and authorisations to ensure compatibility. © 2023, The Authors. All rights reserved.

关键词： Access control

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