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2nd International Conference on Software Engineering and Information Technology, ICoSEIT 2024

作者： Karnyoto, Andrea Stevens Isnan, Mahmud Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350317503

There is still a severe malaria problem worldwide, particularly in regions with limited access to diagnostic tools. It is crucial to develop a system for detecting malaria in blood cells. This paper presents a hybrid Convolutional Neural Network (CNN), and as a classifier, we use a Support Vector Machine (SVM) framework for the automated detection of malaria parasites in blood cell images. The proposed system leverages the strengths of CNNs in feature extraction and representation learning from images, combined with the discriminative power of SVMs for classification. Initially, CNN extracts intricate features from blood cell images, capturing essential patterns indicative of malaria infection. Subsequently, the extracted features are used to train an SVM classifier, enabling accurate discrimination between parasitized and uninfected blood cells. Experimental dataset evaluations were obtained from the Lister Hill National Center for Biomedical Communications website of the National Library of Medicine. The proposed model achieves a better f1-score, outperforming individual CNN or SVM models, around 0.015 compared to individual CNN models and 0.27 compared to individual SVM models. This hybrid CNN-SVM methodology offers a promising solution for accurately and efficiently detecting malaria parasites in blood cell images. © 2024 IEEE.

关键词： Support vector machines

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A Sub-element Adaptive Shock Capturing Approach for Discontinuous Galerkin Methods

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Communications on Applied Mathematics and Computation 2023年第2期5卷 679-721页

作者： Johannes Markert Gregor Gassner Stefanie Walch Department for Mathematics and Computer Science University of CologneWeyertal 86-9050931 CologneGermany Department for Mathematics and Computer Science Center for Data and Simulation ScienceUniversity of CologneWeyertal 86-9050931 CologneGermany I.Physikalisches Institut Center for Data and Simulation ScienceUniversity of CologneZülpicher Str.7750937 CologneGermany

In this paper,a new strategy for a sub-element-based shock capturing for discontinuous Galerkin(DG)approximations is *** idea is to interpret a DG element as a col-lection of data and construct a hierarchy of low-to-high-order discretizations on this set of data,including a first-order finite volume scheme up to the full-order DG *** dif-ferent DG discretizations are then blended according to sub-element troubled cell indicators,resulting in a final discretization that adaptively blends from low to high order within a single DG *** goal is to retain as much high-order accuracy as possible,even in simula-tions with very strong shocks,as,e.g.,presented in the Sedov *** framework retains the locality of the standard DG scheme and is hence well suited for a combination with adaptive mesh refinement and parallel *** numerical tests demonstrate the sub-element adaptive behavior of the new shock capturing approach and its high accuracy.

关键词： High-order methods Discontinuous Galerkin spectral element method Finite volume method Shock capturing Astrophysics Stellar physics

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Menstrual Date Prediction Model Utilizing Daily post sentiment data on Social Media for Modeling

Menstrual Date Prediction Model Utilizing Daily post sentime...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Ruengdat, Sunisa Leklert, Panutchakorn Kongkachandra, Rachada Songmuang, Pokpong Thammasat University Data Science And Innovation College Of Interdisciplinary Studies Bangkok Thailand Thammasat University Department Of Computer Science Bangkok Thailand

ISBN: (纸本)9798350370096

Menstrual cycle prediction is a critical issue for many women, as it can help them plan their daily activities, and prepare for potential physical and emotional changes. However, current methods for predicting menstruation often rely on physiological data such as age, cycle length, and ovulation history, which may not be convenient or accurate for some *** paper presents a comparative study of menstruation prediction models using daily social media sentiment data. The underlying hypothesis is that social media posts can reflect the user's emotions and feelings, which may be related to the menstrual cycle. By tracking emotions from social media posts, it may be possible to monitor potential symptoms associated with the menstrual cycle, such as premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD).The proposed models are evaluated using a dataset of social media posts and self-reported menstruation data. The results demonstrate that the proposed models can effectively predict the onset of menstruation and identify potential symptoms of PMS and PMDD. This work has the potential to contribute to the development of new tools and interventions for women's health and *** experimental research utilized a dataset of posts on X from one woman over a 3-year period (2020-2023), covering both normal and abnormal menstrual cycles. The challenge of this research was to transform ordinary text that could be posted on social media but could not be used for prediction into text that could be used for in-depth prediction and analysis. This was achieved through two processes: 1) Sentiment Analysis using the WangchanBERTa model to determine the sentiment of the posts, which achieved an accuracy of 61.7%;2) the Time Series Forecasting process for predicting the date of menstruation using the Random Forest model was the most appropriate if compared to Linear Regression and SVR, which is considered to be the most accurate. Challenge new faces This is bec

关键词： Forecasting

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HealthBridge: A Decentralized and Interoperable Healthcare App for Rare Disease Communities

HealthBridge: A Decentralized and Interoperable Healthcare A...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Ziyuan Wang, Yiwei Liang, Anda Van Devanter, Wyn Zhang, Peng Vanderbilt University Department of Computer Science NashvilleTN United States Institute Vanderbilt University Department of Computer Science and Data Science NashvilleTN United States

ISBN: (纸本)9798350337488

Traditional centralized database systems share many weaknesses, including the overhead of sharing medical documentation, data fragmentation, and susceptibility to centralized data attacks. Those problems would especially influence rare disease patient communities given their higher need for medical data interoperability and security. In this paper, we present an alternative approach to managing healthcare data that is tailored to the rare disease community. Specifically, we design and implement HealthBridge, a decentralized and interoperable mobile application with distributed ledger technology. The underlying permissioned network utilizes Hyperledger Fabric and has a modular design, empowering efficient data transactions directly between trusted entities with predefined smart contracts that regulate access permissions. For the application design, we focus on the needs of the rare disease community, prioritizing ease of use without compromising data security. In particular, our application provides a delegate feature, aimed at supporting rare disease patients with additional needs. Through this feature, if needed, patients can grant pre-defined access permissions to delegates through smart contracts. While the overall implementation of HealthBridge is still in the developmental stages, it presents a proof of concept and a promising system to support the underfunded rare disease community in terms of healthcare interoperability through a permissioned blockchain network. © 2023 IEEE.

关键词： healthcare informatics interoperability mobile application permissioned blockchain rare disease

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Classification of Pigmented Skin Lesions using Early Fusion of Multi-Deep Features and Support Vector Machine

Classification of Pigmented Skin Lesions using Early Fusion ...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Chowdam, Jayanth Panigrahi, Sibarama National Institute of Technology Intelligent Data Analytics Laboratory Department of Computer Science and Engineering Odisha Rourkela India

ISBN: (纸本)9798350369175

One of the primary organ is the skin. It protects and separates humans from various dangers. Nevertheless, the skin is susceptible to damage and can develop pigmented lesions. Manual classification of Pigmented Skin Lesions(PSL) is a major challenge due to availability and bias of physician, time consuming, invasive methods are painful and with elevated expenses. To overcome this, in this paper, two automated computer vision based approaches (using and without using dimensionality reduction) are employed by modelling multi-deep features using Support Vector Machine(SVM) for classification of PSL. The suggested system comprises of two operational stages. Initially, the performance of six Convolutional Neural Network models (VGG16, VGG19, AlexNet, Xception, ResNet50, and EfficientB3) in deep feature extraction and three types of hand-crafted features extraction methods (HOG, LBP, and SIFT) employing SVM are evaluated. The top three CNN models (AlexNet, ResNet50, and EfficientB3) based on accuracy are used to form binary and ternary early fusion hybrid models to classify the skin lesions. In the second stage, combining multiple CNN models resulted in increased dimensionality and number of features. These features are then classified using SVM once in the higher dimension and again by reducing the dimensionality by applying Principal Component Analysis(PCA). The accuracies of the combined models are then compared. It is observed that the ternary early fusion model with SVM (Alexnet+Resnet50+EfficientB3-SVM) produces the highest accuracy of 82.22% with higher number of features whereas the same model while using PCA produces the accuracy of 81.57%. Consequently, the findings indicate the suggested techniques are useful for classification of PSL. © 2024 IEEE.

关键词： Dimensionality reduction

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An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Language Model Inference

An Empirical Study on Cross-lingual Vocabulary Adaptation fo...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Yamaguchi, Atsuki Villavicencio, Aline Aletras, Nikolaos School of Computer Science University of Sheffield United Kingdom Department of Computer Science Institute of Data Science and Artificial Intelligence University of Exeter United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9798891761681

The development of state-of-the-art generative large language models (LLMs) disproportionately relies on English-centric tokenizers, vocabulary and pre-training data. Despite the fact that some LLMs have multilingual capabilities, recent studies have shown that their inference efficiency deteriorates when generating text in languages other than English. This results in increased inference time and costs. Cross-lingual vocabulary adaptation (CVA) methods have been proposed for adapting models to a target language aiming to improve downstream performance. However, the effectiveness of these methods on increasing inference efficiency of generative LLMs has yet to be explored. In this paper, we perform an empirical study of five CVA methods on four generative LLMs (including monolingual and multilingual models) across four typologically-diverse languages and four natural language understanding tasks. We find that CVA substantially contributes to LLM inference speedups of up to 271.5%. We also show that adapting LLMs that have been pre-trained on more balanced multilingual data results in downstream performance comparable to the original models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Implementation of Fine-Tuned BERT for Enzyme Classification Based on Gene Ontology 11

Implementation of Fine-Tuned BERT for Enzyme Classification ...

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11th International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2024

作者： Henry, Matthew Martianus Kenneth, Christian Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331520311

Enzymes are biocatalysts with vital roles in biological functions and many industrial applications. Diverse enzymes are classified using Enzyme Commission (EC) nomenclature, making differentiation challenging. On the other hand, another biological information, gene ontology (GO), can describe the biological aspects of enzymes, covering related biological processes (BP), molecular functions (MF), and their locations within cells (CC). This study proposes a novel EC class and subclass classification of enzymes within the ontology subclass based on their GO semantics using a Bidirectional Encoder Representation of Transformer (BERT). The BERT model is first fine-tuned using the preprocessed GO term name and definition, with the enzymes in each ontology class (BP, MF, or CC) are also divided based on how the GO assigned, either through manual annotation (NONIEA) or electronically inferred (IEA). BERT successfully obtained 0.93, 0.60, 0.99, 0.90, 0.40, and 0.35 F1 scores during fine-tuning for BP IEA, BP NONIEA, MF IEA, MF NONIEA, CC IEA, and CC NONIEA, respectively. On the test set, the fine-tuned BERT significantly outperformed GOntoSim, a framework to calculate semantic similarity based on classical information theory, in EC class classification across all metrics with less inference time in all ontology subclass. Expanded further to the EC subclass, BERT can classify the enzyme on the EC subclass level in BP IEA and MF IEA ontology subclass. However, longer epochs are needed in fine-tuning. This result shows that the names and definitions of GO terms are distinguishable features in classifying enzymes as an alternative to the information content approach. © 2024 IEEE.

关键词： Gene Ontology

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A Blockchain-Based Architecture for Interoperable Healthcare data Exchange

A Blockchain-Based Architecture for Interoperable Healthcare...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Sahi, Niranjan Liang, Anda McHale, Sean Van Devanter, Wyn Zhang, Peng Vanderbilt University Department of Computer Science NashvilleTN United States Vanderbilt University Department of Computer Science and Data Science Institute NashvilleTN United States

ISBN: (纸本)9798350337488

Patients and healthcare providers often face challenges when dealing with creating, reading, updating, sharing, and deleting operations on individual healthcare data due to incompatible electronic medical record systems and the inability to provide necessary data. These challenges can drastically prolong both a patient's diagnosis time and the time until the operation. There are many informational discrepancies when attempting to provide adequate healthcare background - especially with new providers when a full healthcare history is required. To aid in increasing interoperability, we propose a decentralized data-sharing approach. While current centralized solutions own patients' healthcare data, we seek to put data back in the hands of the patients using blockchain as a decentralized database. In essence, our data-sharing architecture and set of rules leverage a permissioned blockchain network, Hyperledger Fabric, to give each patient on the network the ability to own and control the flow of their healthcare data. © 2023 IEEE.

关键词： data sharing healthcare interoperability multisignature transactions permissioned blockchain

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Cognitive Detection of Anomalies in Autonomous In-Vehicle Network Communication

Cognitive Detection of Anomalies in Autonomous In-Vehicle Ne...

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2024 International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Firasta, Suzen Srivastava, Yash Rahul Rao, Vidya Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Manipal India

ISBN: (纸本)9798350363289

The research addresses the critical issue of detecting anomalies in autonomous in-vehicle network communication, focusing on the vulnerability of infotainment systems to denial-of-service attacks. The aim is to explore and compare traditional Machine Learning (ML) and advanced Deep Learning (DL) models for the detection of DoS attacks within Controller Area Network (CAN) communications. By evaluating the performance of various ML and DL algorithms, including Decision Tree, Logistic Regression, RandomForest, Feed Forward Neural Networks, and LSTM architectures, the study aims to enhance intrusion detection capabilities in vehicular communication systems. The significance of the research lies in identifying the advantages of DL methods in capturing complex patterns and temporal dependencies within the CAN network, thus contributing to fortifying the security of automotive networks against evolving threats. The results demonstrate the efficacy of the proposed models in detecting and mitigating DoS attacks, with DL methods showing promise in automatic learning and adaptability to intricate network structures. This study provides valuable insights for future research endeavors aimed at enhancing the cybersecurity of autonomous vehicles. © 2024 IEEE.

关键词： Network intrusion

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Secure Online Banking Authentication System Using Time Bound Password 5

Secure Online Banking Authentication System Using Time Bound...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Danthy, Ronson Pratham Pai, K. Rao, Vidya Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Department of Computer Science and Computer Applications Manipal India

ISBN: (纸本)9798350383522

The banking system is a key component of financial transactions and economic expansion in the modern world. The introduction of internet banking, however, has created some brand-new difficulties, particularly in providing safe user authentication. This study investigates the flaws in conventional authentication techniques, including passwords and SMS-based One-Time Passwords (OTP), and suggests a creative fix. By using Time-Based One-Time Password (TOTP) created by an authenticator app and encrypting the user credentials with this as key, the proposed solution improves security. By addressing problems including poor password habits, SMS theft, OTP delivery delays, phishing, and social engineering attacks, this method strengthens the security of online banking. The approach provides a thorough framework for building this secure authentication system by detailing TOTP generation and encryption. This system successfully defends against popular attacks such as replay attacks, brute force attacks, rainbow tables, packet sniffing, and random guessing. © 2024 IEEE.

关键词： Advanced Encryption Standard - Galois Counter Mode (AES-GCM) Authentication Encryption Online Banking Time-Based One-Time Password (TOTP) Two-Factor authentication (2FA)

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