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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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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Enhancing the blockchain interoperability through federated learning with directed acyclic graph

IET Blockchain

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IET Blockchain 2023年第4期3卷 238-248页

作者： Xia, Feng Kaiye, Li Songze, Wu yan, Xin Faculty of Data Science City University of Macau Avenida Padre Tomás Pereira China School of Computer Science and Communication Engineering Jiangsu Univestity Zhenjiang China

The use of federated learning to achieve blockchain interoperability has become a hot topic in research, because it enables data exchange without revealing any private information. However, the previous work, such as ScaleSFL (Asia-CCS, 2022), that has implemented federated learning for blockchain interoperability, the throughput of the framework still cannot support the practical applications. Therefore, a federated learning framework based on Directed Acyclic Graph (DAG) is proposed which utilizes sharding mechanism to enhance the blockchain interoperability. By constructing a weighted context graph based on data correlation, reasonable sharding of the dataset is achieved, thereby improving the efficiency of blockchain interoperability. The experimental results show that the federated framework reduces global computation in federated learning by 30% compared with other schemes, while increasing blockchain throughput by nearly 40%. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Blockchain

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Angle-uniform parallel coordinates

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Computational Visual Media 2023年第3期9卷 495-512页

作者： Kaiyi Zhang Liang Zhou Lu Chen Shitong He Daniel Weiskopf Yunhai Wang School of Computer Science and Technology Shandong UniversityQingdao 266237China National Institute of Health Data Science Peking UniversityBeijing 100191China Visualization Research Center(VISUS) University of Stuttgart70569 StuttgartGermany

We present angle-uniform parallel coordinates,a data-independent technique that deforms the image plane of parallel coordinates so that the angles of linear relationships between two variables are linearly mapped along the horizontal axis of the parallel coordinates *** being a common method for visualizing multidimensional data,parallel coordinates are ineffective for revealing positive correlations since the associated parallel coordinates points of such structures may be located at infinity in the image plane and the asymmetric encoding of negative and positive correlations may lead to unreliable *** address this issue,we introduce a transformation that bounds all points horizontally using an angleuniform mapping and shrinks them vertically in a structure-preserving fashion;polygonal lines become smooth curves and a symmetric representation of data correlations is *** further propose a combined subsampling and density visualization approach to reduce visual clutter caused by *** method enables accurate visual pattern interpretation of data correlations,and its data-independent nature makes it applicable to all multidimensional *** usefulness of our method is demonstrated using examples of synthetic and real-world datasets.

关键词： parallel coordinates multidimensional data deformation correlations

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On the Design of Cancer Tumor Type Prediction using Machine Learning Techniques for Efficient Diagnosis 2

On the Design of Cancer Tumor Type Prediction using Machine ...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Thakkar, Hiren Kumar Kumar, Yogesh Kathirisetty, Nikhila School of Technology Pandit Deendayal Energy University Department of Computer Science and Engineering Gujarat 382007 India Vardhaman College of Engineering Department of Computer Science and Data Science Hyderabad Shamshabad India

ISBN: (纸本)9798350370188

In the recent past, cancer is designated as the life threatening disease causing significant deaths across the globe. Countries are putting efforts for early stage cancer detection, and mortality prediction. Machine Learning (ML) models are proven to efficiently find the correlation among the features for robust predictions over statistical approaches. In this paper, we used kaggle dataset that includes 569 cancer cells and around 30 features cancer cells' classification. The proposed study employs ML models (e.g., binary classifiers) for effective predictive analytics. The evaluation results indicate that Random Forest achieves better results with Area Under Curve (AUC) of 0.99 followed by Decision tree with AUC be 0.92. However, the Logistic Regression (LR) and Support Vector Machine (SVM) fails to capture the generalized behavior of the cancer data for predictive analysis. © 2024 IEEE.

关键词： Classification (of information)

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Latency- and Privacy-Aware Convolutional Neural Network Distributed Inference for Reliable Artificial Intelligence Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 365-377页

作者： Hu, Yuhao Xu, Xiaolong Qi, Lianyong Zhou, Xiaokang Xia, Xiaoyu Nanjing University of Information Science and Technology School of Software Nanjing210044 China College of computer science and technology Qingdao266850 China Shiga University Faculty of Data Science Hikone522-8522 Japan RMIT University School of Computing Technologies MelbourneVIC3000 Australia

Reliable artificial intelligence (AI) systems not only propose a challenge on providing intelligent services with high quality for customers but also require customers' privacy to be protected as much as possible during process of the services. Given the ultrahigh computing load brought by deep-learning-based intelligent services to edge devices and the ultralong distance between edge and cloud, low-latency requirement of intelligent services is hard to meet in single edge computing or cloud computing. Edge-cloud collaborative inference of deep neural networks (DNNs) is considered a feasible solution to the problem. However, former work has not reduced the inference latency to the greatest extent and has not considered privacy protection in distributed systems. To solve the problem, we first establish a novel queue mechanism. Then, the convolution layer split decisions are made based on deep reinforcement learning (DRL) to realize the parallel inference of convolutional neural networks (CNNs) for inference latency reduction. Next, for each CNN, the partition decision is made based on brute force algorithm to further reduce inference latency and protect customers' privacy. Finally, simulation results show that our method performs better than the existing other methods. © 2020 IEEE.

关键词： Cloud computing

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LLM-GAN: Constructing Generative Adversarial Network Through Large Language Models for Explainable Fake News Detection

LLM-GAN: Constructing Generative Adversarial Network Through...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Yifeng Gu, Zhouhong Zhang, Siwei Zheng, Suhang Wang, Tao Li, Tianyu Feng, Hongwei Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Alibaba Group China

ISBN: (纸本)9798350368741

Explainable fake news detection predicts the authenticity of news items with annotated explanations. Today, Large Language Models (LLMs) are known for their powerful natural language understanding and explanation generation abilities. However, using LLMs for explainable fake news detection remains two main challenges. Firstly, fake news appears reasonable and could easily mislead LLMs, leaving them unable to understand the complex news-faking process. Secondly, utilizing LLMs for this task would generate correct and incorrect explanations, requiring abundant labor in the loop. In this paper, we propose LLM-GAN, a novel framework that utilizes prompting mechanisms to enable an LLM to function as a Generator and a Detector for realistic fake news generation and detection. Extensive experimental results demonstrate LLM-GAN's effectiveness in both prediction performance and explanation quality. © 2025 IEEE.

关键词： Fake News Detection Large Language Models Prompt Learning

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Vision: Supporting Citizens in Adopting Privacy Enhancing Technologies 23

Vision: Supporting Citizens in Adopting Privacy Enhancing Te...

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2023 European Symposium on Usable Security, EuroUSEC 2023

作者： Shams, Shirin Reinhardt, Delphine University of Göttingen Institute of Computer Science Germany University of Göttingen Institute of Computer Science Campus Institute Data Science Germany

ISBN: (纸本)9798400708145

We have witnessed an alarming growth in collecting citizens' information by businesses and organizations. The more citizens' information they collect, the greater their ability to utilize this knowledge for their own interests, often at the expense of society members. Despite the efforts made by privacy ecosystem members (such as researchers and regulatory entities) to protect citizens' privacy against data collectors, the low adoption of Privacy Enhancing Technologies (PETs) by citizens remains concerning. To address these concerns, this vision paper proposes a user-centred approach to develop a tool that assists citizens in adopting PETs effectively. To this end, we plan to (1) understand the factors contributing to low PETs adoption via methods such as focus groups and in-depth interviews, (2) design an interactive tool to support citizens in adopting PETs in a user-friendly manner, and (3) evaluate the tool's effectiveness through usability testing. The outcome will serve as an interactive tool which first receives the privacy concerns and needs of each user and then provides personalised PET recommendations accordingly. © 2023 ACM.

关键词： Human computer interaction

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An Improved Honey Badger Algorithm through Fusing Multi-Strategies

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computers, Materials & Continua 2023年第8期76卷 1479-1495页

作者： Zhiwei Ye Tao Zhao Chun Liu Daode Zhang Wanfang Bai School of Computer Science Hubei University of TechnologyWuhan430068China Xining Big Data Service Administration Xining810000China

TheHoney Badger Algorithm(HBA)is a novelmeta-heuristic algorithm proposed recently inspired by the foraging behavior of honey *** dynamic search behavior of honey badgers with sniffing and wandering is divided into exploration and exploitation in HBA,which has been applied in photovoltaic systems and optimization problems ***,HBA tends to suffer from the local optimum and low *** alleviate these challenges,an improved HBA(IHBA)through fusing multi-strategies is presented in the *** introduces Tent chaotic mapping and composite mutation factors to HBA,meanwhile,the random control parameter is improved,moreover,a diversified updating strategy of position is put forward to enhance the advantage between exploration and *** is compared with 7 meta-heuristic algorithms in 10 benchmark functions and 5 engineering *** Wilcoxon Rank-sum Test,Friedman Test and Mann-WhitneyU Test are conducted after *** results indicate the competitiveness and merits of the IHBA,which has better solution quality and convergence *** source code is currently available from:https://***/zhaotao789/IHBA.

关键词： Honey Badger Algorithm multi-strategies fusion tent chaotic mapping compound random factors

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Artificial Intelligence-Based Student Computational Thinking Analysis Model (AI-SCTAM) for Curriculum Development 2

Artificial Intelligence-Based Student Computational Thinking...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Sowmiya, S. Umar Farook, R. Giridharan, B. Shalini, A. Airtifical Intellegence and Data Science Karpagam Academy Higher Education Coimbatore India Airtifical Intellegence and Data Science Karpagam Academy Higher Education India Computer Science and Engineering Karpagam Academy Higher Education Coimbatore India

ISBN: (纸本)9798331505745

Computational thinking (CT) has been widely used to improve students' comprehension of mathematical and scientific subjects. However, there is a scarcity of information available regarding the challenges faced by students and the development of their conceptual knowledge in these learning environments, particularly in relation to the integrated curriculum. There is a need to help with course-level curriculum development to improve teaching and learning. Hence, this paper proposes an artificial intelligence-based student computational thinking analysis model (AI-SCTAM) to predict student engagement in learning and improve curriculum development. This research attempted to determine the association between student engagement measures using the machine learning (ML) method to determine the degrees of student involvement. The ML divides students into two groups: highly engaged and poorly engaged, according to the criteria used to measure their degree of involvement. The findings of the correlation study showed a positive association between engagement measures and performance. Analyzing the relationship between student engagement and performance will guide the development of course materials and activities. STEM education metrics based on engagement levels will be proposed to personalize students' learning experiences. © 2025 IEEE.

关键词： Curricula

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