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arXiv 2024年

作者： Diera, Andor Galke, Lukas Scherp, Ansgar Ulm University Data Science and Big Data Analytics Helmholtzstrasse 16 Ulm89081 Germany University of Southern Denmark Dept of Mathematics and Computer Science Campusvej 55 Odense5230 Denmark

Low isotropy in an embedding space impairs performance on tasks involving semantic inference. Our study investigates the impact of isotropy on semantic code search performance and explores post-processing techniques to mitigate this issue. We analyze various code language models, examine isotropy in their embedding spaces, and its influence on search effectiveness. We propose a modified ZCA whitening technique to control isotropy levels in embeddings. Our results demonstrate that Soft-ZCA whitening improves the performance of pre-trained code language models and can complement contrastive fine-tuning. © 2024, CC BY.

关键词： Embeddings

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Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking Randomised Optimisation Algorithms

Deploying DAPHNE Computational Intelligence on EuroHPC Vega ...

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International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom)

作者： Aleš Zamuda Mark Dokter Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia KNOW-CENTER GmbH Research Center for Data-Driven Business & Big Data Analytics Graz Austria

ISBN: (数字)9798350388459

ISBN: (纸本)9798350388466

This paper presents a deployment of DAPHNE (Integrated data Analysis Pipelines for Large-Scale data Management, HPC and Machine Learning) for Computational Intelligence (CI). As CI progresses, e.g. through effort in human societies like the IEEE Computational Intelligence Society (CIS), the latest advances have become more widely popularized. Likewise, with the accessibility of supercomputing resources to advance CI, like with infrastructures such as EuroHPC machines including Vega, this paper demonstrates deployment of Randomised Optimisation Algorithms (ROAs) as a subset of CI algorithms, on EuroHPC Vega. As the Vega is a cluster of distributed nodes, a successful computation on several nodes at once in parallel and concurrently, requires an orchestrated deployment across the nodes, batched using Slurm in this paper. An example ROA benchmarking scenario is then presented in this scope.

关键词： data analysis Pipelines Clustering algorithms Text summarization Machine learning Benchmark testing Timing

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Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

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Computers, Materials & Continua 2024年第8期80卷 2721-2741页

作者： Tareq Hamadneh Khalid Kaabneh Ibraheem AbuFalahah Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Sayan Omarov Zeinab Monrazeri Frank Werner Mohammad Dehghani Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Department of Big Data Analytics and Data Science Software Development Company《QazCode》Almaty050063Kazakhstan Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization(MFO),inspired by the unique behaviors observed in magnificent frigatebirds in their natural *** foundation of MFO is based on the kleptoparasitic behavior of these birds,where they steal prey from other *** this process,a magnificent frigatebird targets a food-carrying seabird,aggressively pecking at it until the seabird drops its *** frigatebird then swiftly dives to capture the abandoned prey before it falls into the *** theoretical framework of MFO is thoroughly detailed and mathematically represented,mimicking the frigatebird’s kleptoparasitic behavior in two distinct phases:exploration and *** the exploration phase,the algorithm searches for new potential solutions across a broad area,akin to the frigatebird scouting for vulnerable *** the exploitation phase,the algorithm fine-tunes the solutions,similar to the frigatebird focusing on a single target to secure its *** evaluate MFO’s performance,the algorithm is tested on twenty-three standard benchmark functions,including unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** results from these evaluations highlight MFO’s proficiency in balancing exploration and exploitation throughout the optimization *** studies with twelve well-known metaheuristic algo-rithms demonstrate that MFO consistently achieves superior optimization results,outperforming its competitors across various *** addition,the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications,thereby validating its practical utility and robustness.

关键词： Optimization metaheuristic magnificent frigatebirds exploration exploitation

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A transformer-based unified multimodal framework for Alzheimer's disease assessment

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Computers in Biology and Medicine 2024年 180卷 108979页

作者： Yu, Qi Ma, Qian Da, Lijuan Li, Jiahui Wang, Mengying Xu, Andi Li, Zilin Li, Wenyuan Department of Big Data in Health Science School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital Zhejiang University School of Medicine Zhejiang Hangzhou China School of Mathematics and Statistics Northeast Normal University Jilin Changchun130024 China

In Alzheimer's disease (AD) assessment, traditional deep learning approaches have often employed separate methodologies to handle the diverse modalities of input data. Recognizing the critical need for a cohesive and interconnected analytical framework, we propose the AD-Transformer, a novel transformer-based unified deep learning model. This innovative framework seamlessly integrates structural magnetic resonance imaging (sMRI), clinical, and genetic data from the extensive Alzheimer's Disease Neuroimaging Initiative (ADNI) database, encompassing 1651 subjects. By employing a Patch-CNN block, the AD-Transformer efficiently transforms image data into image tokens, while a linear projection layer adeptly converts non-image data into corresponding tokens. As the core, a transformer block learns comprehensive representations of the input data, capturing the intricate interplay between modalities. The AD-Transformer sets a new benchmark in AD diagnosis and Mild Cognitive Impairment (MCI) conversion prediction, achieving remarkable average area under curve (AUC) values of 0.993 and 0.845, respectively, surpassing those of traditional image-only models and non-unified multimodal models. Our experimental results confirmed the potential of the AD-Transformer as a potent tool in AD diagnosis and MCI conversion prediction. By providing a unified framework that jointly learns holistic representations of both image and non-image data, the AD-Transformer paves the way for more effective and precise clinical assessments, offering a clinically adaptable strategy for leveraging diverse data modalities in the battle against AD. © 2024 Elsevier Ltd

关键词： Neuroimaging

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A Novel Machine Learning Classifier Based on Genetic Algorithms and data Importance Reformatting

arXiv

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arXiv 2024年

作者： Alkhayyat, A.K. Hewahi, N.M. Big Data Science and Analytics Program College of Science University of Bahrain Bahrain Department of Computer Science College of Information Technology University of Bahrain Bahrain

In this paper, a novel classification algorithm that is based on data Importance (DI) reformatting and Genetic Algorithms (GA) named GADIC is proposed to overcome the issues related to the nature of data which may hinder the performance of the Machine Learning (ML) classifiers. GADIC comprises three phases which are data reformatting phase which depends on DI concept, training phase where GA is applied on the reformatted training dataset, and testing phase where the instances of the reformatted testing dataset are being averaged based on similar instances in the training dataset. GADIC is an approach that utilizes the exiting ML classifiers with involvement of data reformatting, using GA to tune the inputs, and averaging the similar instances to the unknown instance. The averaging of the instances becomes the unknown instance to be classified in the stage of testing. GADIC has been tested on five existing ML classifiers which are Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT), and Naïve Bayes (NB). All were evaluated using seven open-source UCI ML repository and Kaggle datasets which are Cleveland heart disease, Indian liver patient, Pima Indian diabetes, employee future prediction, telecom churn prediction, bank customer churn, and tech students. In terms of accuracy, the results showed that, with the exception of approximately 1% decrease in the accuracy of NB classifier in Cleveland heart disease dataset, GADIC significantly enhanced the performance of most ML classifiers using various datasets. In addition, KNN with GADIC showed the greatest performance gain when compared with other ML classifiers with GADIC followed by SVM while LR had the lowest improvement. The lowest average improvement that GADIC could achieve is 5.96%, whereas the maximum average improvement reached 16.79%. © 2024, CC BY.

关键词： Adversarial machine learning

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Towards a Transportable Causal Network Model Based on Observational Healthcare data 2

Towards a Transportable Causal Network Model Based on Observ...

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2nd AIxIA Workshop on Artificial Intelligence for Healthcare, HC@AIxIA 2023

作者： Bernasconi, Alice Zanga, Alessio Lucas, Peter J.F. Scutari, Marco Stella, Fabio University of Milano - Bicocca Milan Italy Evaluative Epidemiology Unit Department of Epidemiology and Data Science Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy Data Science and Advanced Analytics F. Hoffmann - La Roche Ltd Basel Switzerland University of Twente Enschede Netherlands Lugano Switzerland

Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-world observational data used to train and validate these models are almost always affected by biases that can strongly impact the outcomes validity: two examples are values missing not-at-random and selection bias. Addressing them is a key element in achieving transportability and in studying the causal relationships that are critical in clinical decision making, going beyond simpler statistical approaches based on probabilistic association. In this context, we propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model to estimate the cardiovascular risk of adolescent and young females who survived breast cancer. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability, and provides a prognostic model that outperforms competing machine learning methods. © 2023 CEUR-WS. All rights reserved.

关键词： Risk assessment

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Deep Learning Pipeline for Blood Cell Segmentation, Classification and Counting 32

Deep Learning Pipeline for Blood Cell Segmentation, Classifi...

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32nd Irish Conference on Artificial Intelligence and Cognitive science, AICS 2024

作者： Rao, Abhijeet Kho, Kiang Wei Mykytiv, Vitaliy Visentin, Andrea School of Computer Science & IT University College Cork Ireland Biophotonics@Tyndall IPIC Tyndall National Institute Ireland Hematology Unit Cork University Hospital Ireland SFI Insight Centre for Data Analytics University College Cork Ireland

Blood cell analysis, an important component of modern medicine, provides critical insights into a range of health conditions, right from minor infections to blood disorders such as leukemia. Traditional methods such as flow cytometry have been very effective in blood cell counting but are often reliant on expensive instruments, specialised reagents, and expert operators, thereby limiting their accessibility. To combat these challenges, this research explores the use of advanced deep learning methods to develop a robust and scalable pipeline for blood cell segmentation, classification, and counting. The proposed pipeline automates the entire process of generating high-precision segmentation masks and classifying blood cells into red blood cells or white blood cells by leveraging Meta AI’s Segment Anything Model and a Convolutional Neural Network. Apart from employing a host of image processing operations, the study also addresses challenges such as low image quality and data scarcity, which leads to class imbalance, by employing techniques such as image contrast enhancement and data augmentation. The pipeline can provide accurate cell counting from simple blood smears. The pipeline was then adapted to blood smear images collected by our research team through different instrumentation using transfer learning applied to the deep learning classifier. With over 95% accuracy in blood cell counts across different datasets, the pipeline exhibits a high degree of adaptability, which ultimately validates its potential for broader clinical applications. © 2024 Copyright for this paper by its authors.

关键词： Blood Smears Convolutional Neural Network Deep Learning Segment Anything Model Transfer learning

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Empirical Analysis of Nature-Inspired Algorithms for Autism Spectrum Disorder Detection Using 3D Video dataset

arXiv

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arXiv 2025年

作者： Panchal, Aneesh Khan, Kainat Katarya, Rahul Department of Computational and Data Sciences Indian Institute of Science Karnataka Bangalore560012 India Big Data Analytics and Web Intelligence Laboratory Department of Computer Science & Engineering Delhi Technological University Delhi110042 India

Autism Spectrum Disorder (ASD) is a chronic neurodevelopmental disorder symptoms of which includes repetitive behaviour and lack of social and communication skills. Even though these symptoms can be seen very clearly in social but a large number of individuals with ASD remains undiagnosed. In this paper, we worked on a methodology for detection of ASD from 3-dimensional walking video dataset, utilizing supervised machine learning (ML) classification algorithms and using nature-inspired optimization algorithms for feature extraction from dataset. The proposed methodology involve the classification of ASD using supervised ML classification algorithm and extracting important and relevant features from the dataset using nature-inspired optimization algorithms. We also included the ranking coefficients for finding the initial leading particle. This selection of particle significantly reduces the computation time and hence, improving the total efficiency and accuracy for the ASD detection. To evaluate the efficiency of the proposed methodology, we deployed various combination of classification algorithm and nature-inspired algorithms resulting in an outstanding classification accuracy of 100% using random forest classification algorithm and gravitational search algorithm for feature selection. The application of the proposed methodology with different datasets would enhance the robustness and generalizability of the proposed methodology. Due to high accuracy and less total computation time, the proposed methodology will offer a significant contribution to the medical and academia fields, providing a foundation for future research and advancements in ASD diagnosis. Copyright © 2025, The Authors. All rights reserved.

关键词： Diseases

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Volunteer rescue dispatch during the Coronavirus Disease 2019 epidemic:Using the recruitment of volunteers for pneumonia epidemic prevention and control in Chun’an County as an example

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Journal of Safety science and Resilience 2022年第4期3卷 330-339页

作者： Liting Chen Shengqun Chen Jing Zheng Jianqing Gao College of Electronics and Information Science Fujian Jiangxia UniversityFuzhou 350108China Research Institute for Data Analysis and Intelligent Decision-Making Fujian Jiangxia UniversityFuzhou 350108China School of Information Engineering Fujian Business UniversityFuzhou 350012China Fujian Provincial Universities Engineering Research Center of Big Data Analytics for Business Intelligence Fujian Business UniversityFuzhou 350012China

To solve the problem of volunteer dispatch during the Coronavirus Disease 2019(COVID-19)epidemic,a many-to-many two-sided matching volunteer dispatch method based on an improved predator-search algorithm is ***,different evaluation index sets for volunteers and rescue tasks were developed,and weightings were determined using the analytic hierarchy ***,the actual and expected values of the different indicators of the two parties were determined,and the triangular fuzzy number was used to calculate the satis-faction of the two *** on this number,we used a linear weighting method to calculate the combined satisfaction and build a many-to-many two-sided matching model according to the demands of both ***-sequently,an improved predator-search algorithm was used to solve the ***,taking the recruitment of volunteers for pneumonia epidemic prevention and control in Chun’an County as an example,the method proposed in our study was verified.A comparison and analysis of the results further demonstrated the feasibility and advantages of this method.

关键词： Volunteer Many-to-many Two-sided matching Predator-search algorithm

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Evaluation Framework for Large Language Models: Assessing Factual Consistency and Answer Relevancy in Question-Answering Tasks

Evaluation Framework for Large Language Models: Assessing Fa...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： Salman Shareef M P. Sridevi Ponmalar Big Data Analytics SRM Institute of Science and Technology Kattankulathur India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331598488

ISBN: (纸本)9798331598495

The research evaluates a natural language understanding model for question-answering tasks using factual consistency and answer relevancy metrics. A pre-trained Natural Language Inference (NLI) model is used for factual consistency, while sentence-transformer-based cosine similarity measures answer relevancy. The dataset consists of context-question-answer pairs, and results indicate strong performance in factual consistency but challenges in negation and contradictions. The model also demonstrates robust answer relevancy, with some variability in indirect responses. This evaluation framework guides future improvements for more accurate question-answering systems.

关键词： Accuracy Sensitivity Large language models Semantics Training data Machine learning data models Question answering (information retrieval) Knowledge management Context modeling

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