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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Devin Setiawan Andrea Stevens Karnyoto Indo Intan Bens Pardamean Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83.

关键词： Support vector machines Accuracy Head Computational modeling Transfer learning Breast cancer Magnetic heads data models Reliability Image classification

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A Novel Approach of Securing Medical Cyber Physical Systems (MCPS) from DDoS Attacks

A Novel Approach of Securing Medical Cyber Physical Systems...

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International Conference on Big data Intelligence and Computing, dataCom 2022

作者： Gupta, Brij. B. Chui, Kwok Tai Arya, Varsha Gaurav, Akshat International Center for AI and Cyber Security Research and Innovation and Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Lebanese American University Beirut1102 Lebanon Kowloon Hong Kong Department of Business Administration Asia University Taichung Taiwan Ronin Institute MontclairNJ07043 United States

ISBN: (纸本)9789819922321

The term "Cyber-Physical Systems" (CPS) often refers to systems that are both designed and physical, as well as biological. In regard to a CPS, the evolution of physical quantities and distinct software and hardware states are usually distinguishing it over time. Continuous state variables for the physical components interspersed with discrete events may be used to represent them in general. CPS is employed in a variety of industries, including healthcare, because of its efficiency. An MCPS is a medically critical integration of a medical cyber-physical system. Continuous, high-quality treatment is made possible via the employment of these systems. Challenges include interoperability, security/privacy, and high system software assurance in the MCPS architecture. It’s still early days for MCPS, thus, adequate standards and procedures must be established for their security. Also, due to their low processing capability, they are susceptible to a wide variety of cyberattacks. As a result, MCPS devices need defined protocols and paradigms to maintain their security. In this context, this paper aims to propose DDoS attack detection for the MCPS system. We used statistical approaches to identify and mitigate DDoS attack traffic in the MCPS system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cyber Physical System

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Identifying Source Speakers for Voice Conversion Based Spoofing Attacks on Speaker Verification Systems

Identifying Source Speakers for Voice Conversion Based Spoof...

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

作者： Danwei Cai Zexin Cai Ming Li Department of Electrical and Computer Engineering Duke University Durham USA Data Science Research Center Duke Kunshan University Kunshan China

An automatic speaker verification system aims to verify the speaker identity of a speech signal. However, a voice conversion system could manipulate a person’s speech signal to make it sound like another speaker’s voice and deceive the speaker verification system. Most countermeasures for voice conversion-based spoofing attacks are designed to discriminate bona fide speech from spoofed speech for speaker verification systems. In this paper, we investigate the problem of source speaker identification – inferring the identity of the source speaker given the voice converted speech. To perform source speaker identification, we simply add voice-converted speech data with the label of source speaker identity to the genuine speech dataset during speaker embedding network training. Experimental results show the feasibility of source speaker identification when training and testing with converted speeches from the same voice conversion model(s). In addition, our results demonstrate that having more converted utterances from various voice conversion model for training helps improve the source speaker identification performance on converted utterances from unseen voice conversion models.

关键词： Training Protocols Closed box Signal processing algorithms Signal processing Acoustics data mining

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Optimizing Rare Disease Patient Matching with Large Language Models

Optimizing Rare Disease Patient Matching with Large Language...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Berger, Armin Bashir, Ali Hamza Berghaus, David Mowmita Afsan, Nazia Grigull, Lorenz Fendrich, Lara Hogl, Henriette Ernst, Gundula Schmidt, Ralf Bascom, David Lagones, Tom Anglim Deuber, Tobias Bell, Thiago Lubbering, Max Sifa, Rafet Fraunhofer Iais Department of Media Engineering Germany University of Bonn Department of Computer Science Germany Federal Ministry of Education and Research West-AI Germany Lamarr Institute Germany University Hospital Bonn Center for Rare Diseases Germany Children's Network for Chronic Illnesses and Disabilities Germany Medical School Hannover Department of Medical Psychology Germany Department of Health Queensland Australia Griffith University - School of Medicine Australia

ISBN: (纸本)9798350362480

We present RepLLaMA, a neural ranking model for optimizing patient matching in rare disease communities. Using data from *** consisting of over two thousand profiles and over ten thousand ratings, our bi-encoder architecture maps profiles to 4096-dimensional vectors, enabling efficient similarity computations. The system processes unstructured symptom descriptions and structured responses, incorporating expert-guided LLM enhancements. Results show Top-10 Recall of 49.36% $(\pm 2.03)$, surpassing baselines while maintaining generalization. The implementation provides a scalable solution for rare disease patient matching, addressing computational complexity challenges. © 2024 IEEE.

关键词： Diseases

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Graph-based deep learning models in the prediction of early-stage Alzheimers

Graph-based deep learning models in the prediction of early-...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Bishal Thapaliya Zundong Wu Ram Sapkota Bhaskar Ray Pranav Suresh Santosh Ghimire Vince Calhoun Jingyu Liu Department of Computer Science Georgia State University Atlanta USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Department of Applied Sciences and Chemical Engineering Tribhuvan University Nepal

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairment, and severe dementia. This study investigates the predictive potential of resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity (FC) in understanding Alzheimer's progression. Leveraging deep learning and graph-based models, we introduce two key contributions: 1) a comparative analysis of rs-fMRI time points and FC for Alzheimer's prediction. 2) an innovative graph transformer variant incorporating self-clustering for enhanced prediction accuracy. Experiments on the Alzheimer's Disease Neuroimaging Initiative dataset with 830 subjects reveal two notable conclusions. Firstly, rs-fMRI time points offer limited utility compared to functional network connectivity for transformer-based models, even when considering temporal information. Secondly, a clustering-based attention module proves effective for classifying brain networks in predicting Alzheimer's disease progression, providing valuable insights for future research and clinical applications.

关键词： Deep learning Neuroimaging Analytical models Biological system modeling Predictive models Functional magnetic resonance imaging Transformers Brain modeling Alzheimer's disease engineering in medicine and biology

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Advancing Personalized Medicine: A Scalable LLM-based Recommender System for Patient Matching

Advancing Personalized Medicine: A Scalable LLM-based Recomm...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Berger, Armin Berghaus, David Bashir, Ali Hamza Grigull, Lorenz Fendrich, Lara Lagones, Tom Anglim Högl, Henriette Ernst, Gundula Schmidt, Ralf Bascom, David Deußer, Tobias Bell, Thiago Lübbering, Max Sifa, Rafet Fraunhofer Iais Department of Media Engineering Germany University of Bonn Department of Computer Science Germany Federal Ministry of Education and Research West-AI Germany Lamarr Institute Germany University Hospital Bonn Center for Rare Diseases Germany Department of Health Queensland Australia Griffith University School of Medicine Australia Children's Network for Chronic Illnesses and Disabilities Germany Medical School Hannover Department of Medical Psychology Germany

ISBN: (纸本)9798350362480

This study explores efficient algorithms to enhance user matching in ***, a novel social networking platform designed to connect individuals affected by rare diseases. Our primary objective is to develop a recommender system that identifies and suggests users with similar medical conditions, facilitating meaningful connections within these unique communities. Utilizing textual user profile data, we train sentence embedder models to generate similar embeddings for users that have rated each other high. We investigate various fine-tuning strategies, as well as a hybrid approach between a dense embedder and sparse SPLADE embeddings. Furthermore, we investigate the efficacy of various clustering algorithms, such as TopicBERT for thematic analysis, K-Means for centroid-based grouping, and Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, to reduce the matching complexity and enable better scalability of the platform. © 2024 IEEE.

关键词： Personalized medicine

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BitmapAligner:Bit-Parallelism String Matching with MapReduce and Hadoop

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computers, Materials & Continua 2021年第9期68卷 3931-3946页

作者： Mary Aksa Junaid Rashid Muhammad Wasif Nisar Toqeer Mahmood Hyuk-Yoon Kwon Amir Hussain Department of Computer Science COMSATS University IslamabadWah47040Pakistan Department of Computer Science AIR University IslamabadAerospace and Aviation Campus Kamra43570Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Industrial Engineering and Research Center for Electrical and Information Technology Seoul National University of Science and TechnologyNowon-GuSeoul01811Korea Data Science and Cyber Analytics Research Group Edinburgh Napier UniversityEdinburghEH114DYUK

Advancements in next-generation sequencer(NGS)platforms have improved NGS sequence data production and reduced the cost involved,which has resulted in the production of a large amount of genome *** downstream analysis of multiple associated sequences has become a bottleneck for the growing genomic data due to storage and space utilization issues in the domain of *** traditional string-matching algorithms are efficient for small sized data sequences and cannot process large amounts of data for downstream *** study proposes a novel bit-parallelism algorithm called BitmapAligner to overcome the issues faced due to a large number of sequences and to improve the speed and quality of multiple sequence alignment(MSA).The input files(sequences)tested over BitmapAligner can be easily managed and organized using the Hadoop distributed file *** proposed aligner converts the test file(the whole genome sequence)into binaries of an equal length of the sequence,line by line,before the sequence alignment *** Hadoop distributed file system splits the larger files into blocks,based on a defined block size,which is 128 MB by *** can accurately process the sequence alignment using the bitmask approach on large-scale sequences after sorting the *** experimental results indicate that BitmapAligner operates in real time,with a large number of ***,BitmapAligner achieves the exact start and end positions of the pattern sequence to test the MSA application in the whole genome query *** MSA’s accuracy is verified by the bitmask indexing property of the bit-parallelism extended shifts(BXS)*** dynamic and exact approach of the BXS algorithm is implemented through the MapReduce function of Apache ***,the traditional seeds-and-extend approach faces the risk of errors while identifying the pattern sequences’***,the proposed model resolves the largescale data challen

关键词： Next-generation sequencing multiple sequence alignment MapReduce Hadoop whole-genome big data bit-parallelism

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An Interactive Annotation Method Based on Incremental Learning 2

An Interactive Annotation Method Based on Incremental Learni...

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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Duan, Xiusheng Sun, Guohua Cao, Jingya Zhang, Yuyang Yang, Jingchao Han, Duyu Hebei Polytechnic Institute Department of Artificial Intelligence and Big Data Shijiazhuang China Hebei Provincial University Road Traffic Perception Intelligent Application Technology Research and Development Center Shijiazhuang China Shijiazhuang Tiedao University School of Mechanical Engineering Shijiazhuang China Northeastern University Faculty of Robot Science and Engineering Shenyang China Hebei Jiaotong Vocational and Technical College Shijiazhuang China

ISBN: (纸本)9798350331417

In recent years, military science and technology has been developed rapidly, and new equipments has been equipped in the army. Augmented Reality (AR) technology provides the possibility to solve the problem of equipments operation training. But in the training process, One of the essential technologies is how to location and identify the operation keys. Obviously, the sample set of equipments operation keys is the basis of key recognition, and a certain scale of sample set is an indispensable element to ensure the performance of the recognition model. On the basis of manually establishing a small sample set, this paper puts forward a mechanism based on interactive automatic sample labeling and sample set expanding, the target recognition model was updated by incremental learning method at the same time. © 2023 IEEE.

关键词： data annotation incremental learning interaction sample augmentation

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Convolutional Neural Network and Deep One-Class Support Vector Machine with Imbalanced dataset for Anomaly Network Traffic Detection

Convolutional Neural Network and Deep One-Class Support Vect...

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International Conference on Cyber Security, Privacy and Networking, ICSPN 2022

作者： Chui, Kwok Tai Gupta, Brij B. Chi, Hao Ran Zhao, Mingbo Hong Kong International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Lebanese American University Beirut1102 Lebanon Instituto de Telecomunicações Aveiro3810-193 Portugal School of Information Science & Technology Donghua University Shanghai200051 China

ISBN: (纸本)9783031220173

Anomaly detection of network traffic is important for real-time network monitoring and management. The research challenges for anomaly network traffic detection (ANTD) are attributable to the nature of highly imbalanced dataset of abnormal samples and poor generalization. Generating additional training data or undersampling does not perform well with highly imbalanced dataset. The drives to the common formulation of ANTD as an one-class classification problem. Convolution neural network is utilized for feature extraction. This is followed by a deep one-class support vector machine classifier with customized kernel via multiple kernel learning to address the issue of generalization and overfitting. The deep architecture leverages the performance of traditional support vector machine to a large extent. Performance evaluation reveals that the proposed algorithm achieves accuracy of 97.5%. Ablation studies show that our algorithm enhances the accuracy by 2.52–15.2%. Compared with existing work, our algorithm enhances the accuracy by 3.07%. Several future research directions are discussed for further exploration and analysis. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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Flickr30K-CFQ: A Compact and Fragmented Query dataset for Text-image Retrieval

arXiv

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arXiv 2024年

作者： Liu, Haoyu Song, Yaoxian Wang, Xuwu Zhu, Xiangru Li, Zhixu Song, Wei Li, Tiefeng Research Center for Intelligent Robotics Zhejiang Lab China Department of Engineering Mechanics Center for X-Mechanics Zhejiang University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

With the explosive growth of multi-modal information on the Internet, unimodal search cannot satisfy the requirement of Internet applications. Text-image retrieval research is needed to realize high-quality and efficient retrieval between different modalities. Existing text-image retrieval research is mostly based on general vision-language datasets (e.g. MS-COCO, Flickr30K), in which the query utterance is rigid and unnatural (i.e. verbosity and formality). To overcome the shortcoming, we construct a new Compact and Fragmented Query challenge dataset (named Flickr30K-CFQ) to model text-image retrieval task considering multiple query content and style, including compact and fine-grained entity-relation corpus. We propose a novel LLM-based Query-enhanced method using prompt engineering based on LLM. Experiments show that our proposed Flickr30-CFQ reveals the insufficiency of existing vision-language datasets in realistic text-image tasks. Our LLM-based Query-enhanced method applied on different existing text-image retrieval models improves query understanding performance both on public dataset and our challenge set Flickr30-CFQ with over 0.9% and 2.4% respectively. Our project can be available anonymously in https://***/view/Flickr30K-cfq. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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