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arXiv 2024年

作者： Ma, Siteng Wu, Haochang Lawlor, Aonghus Dong, Ruihai The Insight Centre for Data Analytics School of Computer Science University College Dublin Dublin Ireland School of Electrical and Electronic Engineering University College Dublin Dublin Ireland

Active learning (AL) has found wide applications in medical image segmentation, aiming to alleviate the annotation workload and enhance performance. Conventional uncertainty-based AL methods, such as entropy and Bayesian, often rely on an aggregate of all pixel-level metrics. However, in imbalanced settings, these methods tend to neglect the significance of target regions, eg., lesions, and tumors. Moreover, uncertainty-based selection introduces redundancy. These factors lead to unsatisfactory performance, and in many cases, even underperform random sampling. To solve this problem, we introduce a novel approach called the Selective Uncertainty-based AL, avoiding the conventional practice of summing up the metrics of all pixels. Through a filtering process, our strategy prioritizes pixels within target areas and those near decision boundaries. This resolves the aforementioned disregard for target areas and redundancy. Our method showed substantial improvements across five different uncertainty-based methods and two distinct datasets, utilizing fewer labeled data to reach the supervised baseline and consistently achieving the highest overall performance. Our code is available at https://***/HelenMa9998/Selective Uncertainty AL. Copyright © 2024, The Authors. All rights reserved.

关键词： Redundancy

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Genomic prediction of forage nutritive value in perennial ryegrass

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Grassland Research 2024年第4期3卷 331-346页

作者： Agnieszka Konkolewska Michael Dineen Rachel Keirse Patrick Conaghan Dan Milbourne Susanne Barth Aonghus Lawlor Stephen Byrne Teagasc Crop Science DepartmentCarlowIreland Insight SFI Research Centre for Data Analytics School of Computer ScienceUniversity College DublinDublinIreland VistaMilk SFI Research Centre MooreparkFermoyCo.CorkIreland Teagasc Grassland Science Research DepartmentAnimal and Grassland Research and Innovation CentreMooreparkFermoyIreland Teagasc Grassland Science Research DepartmentAnimal and Grassland Research and Innovation CentreCarlowIreland

Background: Despite its importance to animal production potential, geneticgain for forage nutritive value has been limited in perennial ryegrass (Loliumperenne L.) breeding. The objective of this study was to phenotype a trainingpopulation and develop prediction models to assess the potential of predictingorganic matter digestibility (OMD) and neutral detergent fiber (NDF) withgenotyping-by-sequencing ***: Near infra-red reflectance spectroscopy calibrations for OMD andNDF were developed and used to phenotype a spaced plant training populationof n = 1606, with matching genotype-by-sequencing data, for developinggenomic selection models. F2 families derived from the training populationwere also evaluated for OMD and NDF in sward plots and used to empiricallyvalidate prediction ***: Sufficient genotypic variation exists in breeding populations toimprove forage nutritive value, and spectral bands contributing to calibrationswere identified. OMD and NDF can be predicted from genomic data withmoderate accuracy (predictive ability in the range of 0.51-0.59 and 0.33-0.57,respectively) and models developed on individual plants outperform thosedeveloped from family means. Encouragingly, genomic prediction modelsdeveloped on parental plants can predict OMD in subsequent generationsgrown as competitive ***: These findings suggest that genetic improvement in foragenutritive value can be accelerated through the application of genomic predictionmodels.

关键词： grass nutritive value plant breeding precision agriculture

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Colon Cancer Tissue Classification Using ML 6

Colon Cancer Tissue Classification Using ML

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6th International Conference on Information Systems and Computer Networks, ISCON 2023

作者： Tripathi, Ashish Kumar, Kuldeep Misra, Anuradha Chaurasia, Brijesh Kumar Amity University Lucknow Amity School of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Lucknow India Bond University Center for Data Analytics Bond Business School Gold CoastQLD Australia Pranveer Singh Institute of Technology Department of Computer Science and Engineering Uttar Pradesh Kanpur India

ISBN: (纸本)9798350346961

In this paper, the classification of colon cancer tissues by means of machine learning approaches is evaluated. In today's world, a revolutionary advancement has come in the classification and diagnosis of diseases in the medical and healthcare sectors. Deep learning classifiers and machine learning methods are now broadly applied to accurately diagnose a number of diseases. Cancer is one of the world's most significant roots of death, appealing to the lives of one person out of every six. As per the national library of medicine, the third leading cause of death worldwide is colorectal cancer. Identifying an illness at a premature stage increases the chances of survival. Automated diagnosis and the classification of tissues from images can be completed much more quickly with the use of artificial intelligence. A publicly available IoT dataset CRC-VAL-HE-7K consisting of 7180 images, distributed among nine types of colorectal tissues: background, lymphocytes, adipose, mucus, colorectal adenocarcinoma epithelium, normal colon mucosa, debris, cancer-associated stroma, and, smooth muscle is used after preprocessing. Feature extraction is done by applying Differential-Box-Count on all blocks of images. The dataset is evaluated by these Machine Learning (ML) procedures: K-Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, and Gaussian Naive Bayes. Results show that the performance of Extreme Gradient Boosting is the best and most viable approach.. © 2023 IEEE.

关键词： Image classification

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A Novel Multi-Scale Deep Learning Approach for the Early Detection of Alzheimer's Disease Using fMRI 5

A Novel Multi-Scale Deep Learning Approach for the Early Det...

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5th International Conference on Computer, Big data and Artificial Intelligence, ICCBD+AI 2024

作者： Rehman Butt, Ateeq Ur Hamid, Isma Nawaz, Qamar Mahmood, Tariq Zhang, Xiang Yaqub, Muhammad National Textile University Department of Computer Science Faisalabad Pakistan University of Agriculture Department of Computer Science Faisalabad Pakistan Prince Sultan University Artificial Intelligence and Data Analytics Lab Ccis Riyadh11586 Saudi Arabia Chinese Academy of Sciences Institute of Biophysics Beijing100101 China Hunan University School of Biomedical Sciences Changsha China

ISBN: (纸本)9798331533991

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that remains challenging to diagnose in its early stages. This study presents a novel 3D multi-scale Convolutional Neural Network (CNN) model that utilizes resting-state functional MRI (rs-fMRI) data to classify different stages of Alzheimer's disease. The proposed model integrates multi-scale feature extraction from the hippocampal region, optimizing the detection of fine-grained and global patterns indicative of AD progression. The model was evaluated on a balanced dataset of 300 subjects, applying cross-validation to ensure robustness across multiple binary classification tasks. Our experimental results demonstrate a classification accuracy of up to 93% for distinguishing Alzheimer's Disease from Cognitively Normal subjects. High sensitivity, specificity, and precision were observed across all classification tasks, indicating the model's effectiveness in early-stage Alzheimer's detection. These findings suggest the proposed approach could enhance clinical diagnosis and facilitate timely interventions for AD patients. © 2024 IEEE.

关键词： Convolutional neural networks

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Effective Hybrid Precoder Logic with mm Wave Massive MIMO using Novel Deep Learning Scheme 8

Effective Hybrid Precoder Logic with mm Wave Massive MIMO us...

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8th IEEE International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Ranjith, S. Jesu Jayarin, P. Chandra Sekar, A. Jeppiaar Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Institute of It Saveetha School of Engineering Department of Data Analytics Chennai India St. Joseph's College of Engineering Department of Computer Science Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350347791

For establishing a high efficient signal communications, a Millimeter-Wave-Massive-MIMO (mmWave-MMIMO) logics are helpful and more speculated. Although current hybrid precoding schemes are computationally complex, however, they are not using all of the spatial details. The combination of analog and digital precoding logics are considered to be a significant methodology to deal with complexities over hardware area and the higher consumption of energy in association with combination of signal elements. But the basic degradation of the traditional hybrid-precoding methodologies has some issues such as processing complexities and has a lacking to explore the spatial data regarding the channels. To address these limitations, this paper suggests a Novel Deep Learning Scheme (NDLS), in which it is enabled by mmWave-MMIMO system for successful Hybrid Precoder Logic (HDL). As well as each Precoder selection is used to obtain the The optimised-decoder is perceived in the Novel Deep Learning Scheme as a mapping relation. To be more precise, a Hybrid Precoder Logic is extracted via Novel Deep Learning Scheme training for optimizing the Millimeter- Wave-Massive-MIMO precoding method. Additionally, a detailed simulation results are presented to demonstrate the proposed scheme's superior efficiency and the resulting section demonstrate that the Novel Deep Learning Scheme based methodology is competent of lowering the Bit-Error- Rate (BER) and increasing the effectiveness of the frequency of Millimeter-Wave Massive-MIMO (mmWave- MMIMO), gaining a competitive advantage in faster convergence as compared to traditional schemes while significantly reducing the necessary computational difficulty. © 2023 IEEE.

关键词： Millimeter waves

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Artificial Intelligence Strategies for Accurate Segmentation and Categorization of Unveiling Genetic Disorders in Bioinformatics 2

Artificial Intelligence Strategies for Accurate Segmentation...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Sarathamani, T. Kavitha, K. Thirumoorthi, C. Vagini, K Jayanthi Manikandaprabhu, P. Sumathi, P. Kolkata India Mohan Babu University School of Computing Department of Ai and Ml Sree Sainath Nagar Tirupati India Psg College of Arts and Science Department of Computer Science Tamilnadu Coimbatore India Kpr College of Arts Science and Research Department of Computer science with Data Analytics Tamilnadu Coimbatore India Amrita Vishwa Vidyapeetham School of Computing Department of Computer Science Karnataka Mysuru India Dr. G R Damodaran College of Science Department of Computer Science Tamilnadu Coimbatore India

ISBN: (纸本)9798350368413

Women all across the world are affected by the potentially fatal condition known as breast cancer. According to clinical experts, early cancer detection helps to save lives. Several machine learning algorithms have been developed to classify cancers in order to diagnose breast cancer, and medical image processing has been found to be a beneficial area for increasing classification accuracy. One of the most crucial signs for detecting breast cancer early is micro classifications. Machine learning methods have been employed in numerous studies to address this problem. Various classification algorithms are used to find the presence of breast cancer whether it is Benign or Malignant. For earlier cancer detection and diagnosis, an image enhancement approach is being developed. A machine learning technique is employed in this work to attempt to categorize breast cancer. The purpose of the present research was to increase the accuracy of machine learning methods for disease diagnosis using ML techniques. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimal half-metal band structure for large thermoelectric performance

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Physical Review B 2024年第3期110卷 035150-035150页

作者： Finantius E. M. Rahangiar Adam B. Cahaya Melania S. Muntini Isa Anshori Eddwi H. Hasdeo Department of Physics Research Center for Quantum Physics Department of Physics Faculty of Mathematics and Natural Sciences Department of Physics Faculty of Science and Data Analytics School of Electrical Engineering and Informatics Research Center for Nanosciences and Nanotechnology Department of Physics and Material Science

Half-metal ferromagnets were predicted [Cahaya, Tretiakov, and Bauer, IEEE Trans. Magn. 51, 1 (2015)] to give large thermoelectric performance in antiparallel spin valve configuration. Despite being metals that suffer from the Wiedemann-Franz law, the additional spin degrees of freedom allow for tuning of the thermoelectric properties due to the spin valve enhancement factor. We test this theory and find a mismatch of parameters that gives large thermoelectric performance and large spin valve enhancement factor. As a result, we show that the spin valve setup is useful only for half-metal ferromagnets with initially poor thermoelectric performance. To obtain the largest thermoelectric performance, one still needs to open the band gap.

关键词： Thermal energy harvesting devices Transport phenomena Boltzmann theory

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Breaking the Barrier: Selective Uncertainty-Based Active Learning for Medical Image Segmentation

Breaking the Barrier: Selective Uncertainty-Based Active Lea...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Siteng Ma Haochang Wu Aonghus Lawlor Ruihai Dong The Insight Centre for Data Analytics School of Computer Science University College Dublin Dublin Ireland School of Electrical and Electronic Engineering University College Dublin Dublin Ireland

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database Intrusion Detection Systems (DIDs): Insider Threat Detection via Behaviour-Based Anomaly Detection Systems - A Brief Survey of Concepts and Approaches 1

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2nd International Symposium on Emerging Information Security and Applications, EISA 2021

作者： Khan, Muhammad Imran Foley, Simon N. O’Sullivan, Barry Insight Centre for Data Analytics School of Computer Science and Information Technology University College Cork Cork Ireland Department of Information Security and Communication Technology Norwegian University of Science and Technology Gjøvik Norway

ISBN: (数字)9783030939564

ISBN: (纸本)9783030939557

One of the data security and privacy concerns is of insider threats, where legitimate users of the system abuse the access privileges they hold. The insider threat to data security means that an insider steals or leaks sensitive personal information. database Intrusion detection systems, specifically behaviour-based database intrusion detection systems, have been shown effective in detecting insider attacks. This paper presents background concepts on database intrusion detection systems in the context of detecting insider threats and examines existing approaches in the literature on detecting malicious accesses by an insider to database Management Systems (DBMS). © 2022, Springer Nature Switzerland AG.

关键词： Intrusion detection

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Gender differences in collaboration and career progression in physics

arXiv

引用

arXiv 2024年

作者： She, Mingrong Bachmann, Jan Karimi, Fariba Peel, Leto Department of Data Analytics and Digitalisation School of Business and Economics Maastricht University Maastricht Netherlands Complexity Science Hub Vienna Austria Department of Network and Data Science Central European University Vienna Austria Graz University of Technology Graz8010 Austria

We examine gender differences in collaboration networks and academic career progression in physics. We use the likelihood and time to become a principal investigator (PI) and the length of an author’s career to measure career progression. Utilising logistic regression and accelerated failure time models, we examine whether the effect of collaboration behaviour varies by gender. We find that, controlling for the number of publications, the relationship between collaborative behaviour and career progression is almost the same for men and women. Specifically, we find that those who eventually reach principal investigator (PI) status, tend to have published with more unique collaborators. In contrast, publishing repeatedly with the same highly interconnected collaborators and/or larger number of coauthors per publication is characteristic of shorter career lengths and those that do not attain PI status. We observe that the tie strength is stronger for women than men, and women tend to collaborate in more tightly connected and larger groups than men. Finally, we observe that women are less likely to attain the status of PI throughout their careers and have a lower survival probability compared to men, which calls for policies to close this crucial gap. © 2024, CC BY-NC-SA.

关键词： Logistic regression

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