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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Hossen, Md. Sagar Haque, Imdadul Sarkar, Puza Rani Islam, Md. Ashiqul Fahim, Wasik Ahmmed Khatun, Tania Institut Teknologi Sepuluh Nopember Dept. of Informatics Engineering Surabaya Indonesia Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

Nowadays, Liver Disease (LD) is a very common clinical problem for human health and is related to morbidity and mortality. Nevertheless, an earlier prognosis of LD patients gets a scope to avoid, prior diagnosis and subsequent treatment. This research work attempts to implement a high qualified performer machine learning design to predict LD, the most wanted and unwanted risk factor of LD which could help physicians in classifying risky patients and create an analysis to restrict and control LD. The proposed research study has included all patients, who were identified as having liver diseases. Totally, 6 (six) machine learning algorithms such as Decision Tree(DT), Logistic Regression(LR), Multilayer Perceptron(MLP), Artificial Neural Network(ANN), Random Forest(RF), K Nearest Neighbor classifier(KNN) are selected to predict LD. The location underneath had been utilized to evaluate the accuracy among the six applied models. An overall total of 583 instances had been included in this scholarly research;of the 416 patients are affected by liver illness. The location which defines the receiver operating characteristic (AU ROC) of Logistic Regression, Decision Tree, Multilayer Perceptron, Random Forest, Artificial Neural Network, and K-Nearest Neighbor classifier with 10-fold-cross validation was performed. Furthermore, the reliability of LR, DT, MLP, RF, ANN and KNN with accuracy 72.89%, 81.32%, 60.24%, 86.14%, 75.61%, and 65.52%. The utilization of woodland which is certainly arbitrary within the medical setting may help doctors to detect and classify liver patients for major avoidance, surveillance, quick treatment, and management. LR, DT, MLP, RF, ANN, and KNN formulas are acclimatized to forecast and after analyzing the data set, an increased price of accuracy is achieved. © 2022 IEEE.

关键词： Multilayer neural networks

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Fail-Controlled Classifiers: Do they Know when they don’t Know?

Fail-Controlled Classifiers: Do they Know when they don’t K...

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Pacific Rim International Symposium on Dependable Computing

作者： Tommaso Zoppi Fahad Ahmed Khokhar Andrea Ceccarelli Leonardo Montecchi Andrea Bondavalli Dept. of Mathematics and Informatics University of Florence Florence Italy Dept. of Computer Science Norwegian University of Science and Tech Trondheim Norway

ISBN: (数字)9798331540746

ISBN: (纸本)9798331540753

Domain experts are desperately looking to solve decision-making problems by designing and training Machine Learning algorithms that can perform classification with the highest possible accuracy. No matter how hard they try, classifiers will always be prone to misclassifications due to a variety of reasons that make the decision boundary unclear. This complicates the integration of classifiers into critical systems, where misclassifications could directly impact people, infrastructures, or the environment. The paper proposes to consider a classifier as a structural part of the system instead of an individual component to be tested in isolation and included in the system afterward. This allows for omitting those predictions that are suspected to be misclassifications, triggering system-level mitigation strategies. The resulting fail-controlled classifiers (FCCs) are software components that can correctly classify, misclassify, or omit outputs: ideally, they would omit all and only outputs that correspond to misclassifications. After presenting the theoretical foundations of FCCs, the paper proposes metrics to quantify their performance, 5 software architectures for FCCs, and an experimental analysis involving tabular data and image classifiers. Overall, this paper advocates the need for a system and software design in which ML classifiers are not separate components, but should rather be considered building blocks that interact with other components for improved performance.

关键词： Training Measurement Software design Machine learning algorithms Accuracy Software architecture Prevention and mitigation Decision making Software

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Embeddings of Planar Quasimetrics into Directed 1and Polylogarithmic Approximation for Directed Sparsest-Cut 62

Embeddings of Planar Quasimetrics into Directed 1and Polylog...

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62nd IEEE Annual Symposium on Foundations of computer science, FOCS 2021

作者： Kawarabayashi, Ken-Ichi Sidiropoulos, Anastasios National Institute of Informatics Tokyo Japan University of Illinois at Chicago Dept. of Computer Science Chicago United States

ISBN: (纸本)9781665420556

The multi-commodity flow-cut gap is a fundamental parameter that affects the performance of several divide conquer algorithms, and has been extensively studied for various classes of undirected graphs. It has been shown by Linial, London and Rabinovich [20] and by Aumann and Rabani [5] that for general n-vertex graphs it is bounded by O(log n) and the Gupta-Newman-Rabinovich-Sinclair conjecture [13] asserts that it is O(1) for any family of graphs that excludes some fixed minor. We show that the multicommodity flow-cut gap on directed planar graphs is O(log 3n). This is the first sub-polynomial bound for any family of directed graphs of super-constant treewidth. We remark that for general directed graphs, it has been shown by Chuzhoy and Khanna [11] that the gap is Ω(n1/7), even for directed acyclic graphs. As a direct consequence of our result, we also obtain the first polynomial-time polylogarithmic-approximation algorithms for the Directed Non-Bipartite Sparsest-Cut, and the Directed Multicut problems for directed planar graphs, which extends the long-standing result for undirectd planar graphs by Rao [22] (with a slightly weaker bound). At the heart of our result we investigate low-distortion quasimetric embeddings into directed e 1. More precisely, we construct O(log2n)-Lipschitz quasipartitions for the shortest-path quasimetric spaces of planar digrap hs, which generalize the notion of Lipschitz partitions from the theory of metric embeddings. This construction combines ideas from the theory of bi-Lipschitz embeddings, with tools form data structures on directed planar graphs. © 2022 IEEE.

关键词： Approximation algorithms

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Efficient GPU-based Gridding and Segmentation of Microarray Images via K-Means and Fuzzy C-Means

Efficient GPU-based Gridding and Segmentation of Microarray ...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Eric Clapten, J. Sasimekala, M.M. Rajeswari, C. Sanjay Kumar, K. Communication Engineering Department of Electronics Chennai India Department of Biomedical Engineering Chennai India Chennai Institute of Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348910

The analysis of microarray image helps in identifying new genes that enables us to understand their functions and gene expression levels simultaneously at different conditions. This is also useful in diagnosing the various diseases and helps in develop the treatment for the required disease. In general it has 3 stages, the first one is gridding, and the second segmentation and the third one is intensity extraction. In this paper we are going to discuss various machine learning techniques in segmenting the microarray images. This proposed algorithm eliminates the noise and enhances the image quality by finding the nearest spots in segmentation and enhancing the spot using KNN algorithm. The performance can be improved further by the efficient utilization of a graphics processing unit (GPU). It is also shown that the method can be employed to produce a model on health based on a PET-MRI dataset which can be utilized to automatically detect pathology in the imaging. © 2023 IEEE.

关键词： Graphics processing unit

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Design of Automatic Radiation Detection System as a Part of Radiation Protection in Linear Accelerator Facilities 12

Design of Automatic Radiation Detection System as a Part of ...

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12th International Scientific Conference on Communication and Information Technologies, KIT 2023

作者： Dugas, Martin Ferencik, Norbert Hudak, Radovan Nadova, Zuzana Kolembusova, Petra Steingartner, William Technical University of Košice Dept. of Biomedical Engineering Košice Slovakia Pavol Jozef Šafárik University in Košice Faculty of Science Slovakia Technical University of Košice Dept. of Computers and Informatics Košice Slovakia

ISBN: (纸本)9798350338393

The main objective of this study is the development of an automatic radiation detection system that can be installed at linear accelerator facilities. This system aims to inform the linear accelerator operating staff about the ambient radiation dose rate inside the treatment room. This information might further improve radiation safety. The system is meant to capture residual radiation in the treatment room induced by photonuclear radioactive activation of materials inside the linear accelerator. This study contains current knowledge about ionizing radiation, its dosimetry and detection. It also contains the newest radiation protection laws and radiation protection standards of linear accelerator facilities. The main part of the study is concerned with the design of the detection system - hardware, software, connection and testing of functionality and reliability of the system. The testing was performed by measurement of residual radiation 20 cm away from the source of the linear accelerator. It was performed immediately after applying determined monitor units of photons with energies of 10 MeV and 15 MeV. Measurements were carried out simultaneously using our system and a certified dosimeter RAYSAFE 452. The measured values were compared afterward. Measurement error of 6,08 % and 6,25 %, respectively, was observed. Therefore, the system is arguably reliable and adequate for its purposed use. © 2023 IEEE.

关键词： Ionizing radiation

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Classification of Awareness in Disorders of Consciousness Using Common Spatial Pattern 13

Classification of Awareness in Disorders of Consciousness Us...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Lee, Chaewon Lee, Sunho Annen, Jitka Jeong, Ji-Hoon Massimini, Marcello Casarotto, Silvia Boly, Melanie Bodart, Olivier Thibaut, Aurore Rosanova, Mario Laureys, Steven Gosseries, Olivia Lee, Minji Dept. Data Science The Catholic University of Korea Bucheon Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of Dept. GIGA-Research & Neurology University and University Hospital of Liege Liege Belgium Dept. Data Analysis University of Ghent GhentB9000 Belgium Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical and Clinical Sciences "L. Sacco" University of Milan Milan Italy Wisconsin Institute for Sleep and Consciousness Dept. Psychiatry University of Wisconsin Madison United States Coma Science Group GIGA-Consciousness GIGA Research Center University of Liège Liège Belgium Centre du Cerveau Neurology department University Hospital of Liège Liège Belgium

ISBN: (纸本)9798331521929

Advances in intensive care have improved the survival rate of patients with severe acute brain injury, but diagnostic errors for patients with disorders of consciousness are still high. Accurate diagnosis of these patients is very important because effective treatment can vary depending on the diagnosis. In this study, we propose a framework for classifying unresponsive wakefulness syndrome and minimally conscious state, focusing on awareness. In particular, power spectral density and common spatial patterns were used together, considering that spatial information is a key feature in consciousness. The 16 patients with unresponsive wakefulness syndrome and 14 with minimally conscious state underwent resting-state electroencephalography measurements. In addition, we compared the performance by utilizing each frequency (delta, theta, alpha, beta, gamma bands) related to consciousness. As a result, the highest accuracy of 95.06% was achieved by the EEGNet classifier, especially in the beta frequency band. We demonstrated that spatial information is very important in consciousness, as we observed that classification performance improved when common spatial patterns were used. These results provide insight into various frameworks for diagnosing patients with disorders of consciousness and may help patients survive by increasing the diagnosis rate in the future. © 2025 IEEE.

关键词： Electroencephalography

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Retaliating against cyber-attacks: a decision-taking framework for policy-makers and enforcers of international and cybersecurity law

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International Cybersecurity Law Review 2024年第2期5卷 237-262页

作者： Leventopoulos, Sozon Pipyros, Kosmas Gritzalis, Dimitris Dept. of Informatics Athens University of Economics & Business Athens Greece Zonos Systems Consulting Athens Greece Dept. of Computer Science Philips University Nicosia Cyprus

Cyber warfare is a reality taking on increasing importance. Governments, state-sponsored actors, and non-state sponsored actors have used cyber-attacks as the “weapon of choice” due to their specific characteristics. Cyber-attacks can be highly targeted and focused, even tailored to a specific unit or system, providing limited to no physical destruction (unlike a cruise missile) and potentially resulting in no loss of life. There are several incident response frameworks and approaches that an organization can implement to enhance its security posture. Usually, these will address specific adverse events such as computer security incidents, which in turn are limited in scope and coverage, typically within an organization. Nations have made limited effort in confronting severe cyber-attacks targeting and/or threatening them, as well as in preventing these attacks from being launched. In this work, we identify and discuss a decision-taking framework that may allow state actors to adopt new options against severe cyber-attacks, not always complying with international norms. Such options are neither encouraged nor supported. On the contrary, we discuss them so that the international community is made aware of such potential frameworks. More specifically, by defining clear thresholds, roles, and responsibilities, by introducing a structured chain of command, and by identifying the potential of certain actions, policy makers can recognize an extended decision space that may lead to unpredictable deterrence options against cyber-attacks.

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Fine-tuning a pre-trained Transformers-based model for gene name entity recognition in biomedical text using a customized dataset: case of Desulfovibrio vulgaris Hildenborough

Fine-tuning a pre-trained Transformers-based model for gene ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Bomgni, Alain Bertrand Abdala, Dialo Shrestha Gurung, Bichar Dip Julius Nkenlifack, Marcellin Gadhamshetty, Venkataramana Etienne Gnimpieba, Z. University of South Dakota Dept. of Biomedical Engineering VermillionSD United States University of Dschang Dept. of Computer Science Dschang Cameroon South Dakota School of Mines and Technology Dept. of Civil and Environmental Engineering Rapid CitySD United States

ISBN: (纸本)9798350337488

Gene Name Entity Recognition (NER) plays a crucial role in the realm of biomedical text mining by focusing on the identification and extraction of gene references from scientific literature. Recent advancements in the field-particularly the emergence of pre-trained transformer-based language models like BioBERT-have shown significant promise in the domain of biomedical NER. However, these models are often trained on existing, publicly available datasets, which may not fully capture the nuances of the domain or adequately cover less-studied genes. This study places its primary emphasis on fine-tuning BioBERT specifically for gene NER tasks. To address the limitations associated with current publicly available datasets, we have meticulously crafted a custom dataset. This dataset is thoughtfully constructed through the systematic collection and detailed annotation of a diverse range of biomedical literature from specialized sources. It intentionally includes genes that have been extensively researched, as well as those that have received limited attention in existing corpora. The fine-tuning process involves initializing the BioBERT model with pre-trained weights and then training it on our custom dataset using a sequence tagging approach. To enhance the model's performance, we systematically explore various techniques, including data augmentation, entity-level features, and attention mechanisms. Additionally, we conduct rigorous hyperparameter optimization to maximize the model's accuracy, precision, and recall in gene mention recognition. We thoroughly evaluate the performance of the fine-tuned BioBERT model through a comprehensive set of cross-validation experiments. The results highlight the effectiveness of our tailored dataset in enhancing BioBERT's performance in gene NER. The fine-tuned model achieves impressive F1 scores, precision, and recall, specifically 0.96, 0.95, and 0.98, surpassing previous models when it comes to recognizing the dvu (Desulfovibrio vul

关键词： Gene Name Name Entity Recognition Natural language Processing Pre-trained Language Models Text Mining Transformers

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Special Session: Amplifying Voices Through Animaker Animation: Results from a Longitudinal Study with Latino/a/x Engineering Students 54

Special Session: Amplifying Voices Through Animaker Animatio...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Mejia, Joel Alejandro Hernandez, Sofia Paul, Neethu University of Texas at San Antonio Dept. of Biomedical & Chemical Eng. San AntonioTX United States University of Texas at San Antonio Dept. of Computer Science San AntonioTX United States University of Texas at San Antonio Dept. of Int. Learning & Teaching San Antonio United States

ISBN: (纸本)9798350351507

This special session explores the application of Chicana Feminist Epistemology methodologies in engineering education research with a focus on Latino/a/x students at Hispanic Serving Institutions. By utilizing Platicas and Testimonios as forms of data and data collection approaches in engineering education research, this session aims to foster a more inclusive and empowering research approach while challenging traditional Westernized paradigms. Through the analysis of Animaker videos created from vignettes emerging from these Platicas and Testimonios, participants will have a better understanding of the experiences of Latino/a/x engineering students, emphasizing asset-based methodologies and the creation of multimodal artifacts for research purposes. The research approaches presented in this session seek to amplify marginalized voices, promote critical consciousness, and challenge traditional ways of knowing and doing in engineering education research, encouraging attendees to integrate these methodologies into their research and teaching practices effectively to support diverse student populations. © 2024 IEEE.

关键词： Students

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NLPADADE: Leveraging Natural Language Processing for Automated Detection of Adverse Drug Effects

NLPADADE: Leveraging Natural Language Processing for Automat...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Bomgni, Alain Bertrand Mbotchack Ngale, Claude Epiphanie Aryal, Shiva Nkenlifack, Marcellin Julius Gadhamshetty, Venkataramana Etienne Gnimpieba, Z. University of South Dakota Dept. of Biomedical Engineering VermillionSD United States University of Dschang Dept. of Computer Science Dschang Cameroon South Dakota School of Mines and Technology Dept. of Civil and Environmental Engineering Rapid CitySD United States

ISBN: (纸本)9798350337488

Pharmacovigilance is a systematic and scientifically rigorous discipline that assumes responsibility for the safety of pharmaceuticals, with its primary objective being the mitigation of risks while optimizing the benefits associated with medication usage. This mission-critical undertaking plays an indispensable role in preserving public health. At its core, pharmacovigilance entails the methodical collection and proficient management of data pertaining to medication safety. Additionally, these activities encompass the vigilant scrutiny of data to detect emerging "signals"indicative of new or evolving safety concerns. The expert evaluation of this data facilitates well-informed decision-making regarding matters of drug safety. Furthermore, proactive risk management strategies are deployed to effectively mitigate potential associated risks. In the pursuit of proactive health protection, regulatory actions are swiftly executed. Concurrently, the World Health Organization (WHO) underscores the global importance of establishing a robust pharmacovigilance framework. It advocates for the establishment of a comprehensive pharmacovigilance system, defined as encompassing "the science and activities related to the detection, assessment, understanding, and prevention of adverse effects or any other problem related to drugs or any other healthcare product."In this dynamic landscape, a pivotal question arises: "How can the automated identification and extraction of references to diseases, medications, and adverse effects from clinical notes and biomedical literature be achieved?"Central to this discourse are adverse drug effects (ADEs), which present a formidable public health challenge, manifesting as a significant source of patient morbidity and mortality. To expedite the utilization of real-world data (RWD) for the enhancement of pharmacovigilance practices, our focus has gravitated towards the development of a high-performance natural language processing (NLP) model. This m

关键词： Adverse Drug Effects NLP Pharmacovigilance Text Mining

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