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2024 International Conference on Information Technology research and Innovation, ICITRI 2024

作者： Parulian, Onesinus Saut Aji, Indra Budi Patria, Omega Joel Nugroho, Ariyanto Adi Susanto, Nanang Mariyanto, Totox Salsabila, Nurul Jannah Haris, Muhammad Manik, Lindung Parningotan Universitas Nusa Mandiri Jakarta Computer Science Department Indonesia Universitas Nusa Mandiri Jakarta Research Center for Data and Information Sciences Badan Riset dan Inovasi Nasional Computer Science Department Indonesia

ISBN: (纸本)9798350376210

Rapid development of software significantly facilitates developers in all phases of software development. Developers can easily leverage frameworks, content management systems (CMS), and libraries available in each programming language. With the large and rapid addition of frameworks, CMS, and libraries, developers must be able to choose precisely which ones to use based on the requirements of their software development projects. The utilization of machine learning for segmentation or clustering enables developers to identify groups of frameworks, CMS, and libraries by leveraging features obtained from static analysis, such as the size of codebases, complexities, structures, and other code characteristics. One algorithm that can be used for segmentation is K-means clustering. However, the availability of datasets is crucial as it can offer information about the code characteristics of each software. The use of machine learning on datasets can be an ongoing research area in software development related to utilizing code characteristics in software analysis. This study provides a novel dataset for the domains of software development and machine learning. The dataset we created contains 30 instances and 68 features that can be utilized for related or future research. This research utilizes the Silhouette score to determine the optimal k value. © 2024 IEEE.

关键词： Software design

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Towards Source-Free Domain Adaptive Semantic Segmentation Via Importance-Aware and Prototype-Contrast Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Cao, Yihong Zhang, Hui Lu, Xiao Xiao, Zheng Yang, Kailun Wang, Yaonan College of Computer Science and Electronic Engineering Hunan University Changsha China National Engineering Research Center of Robot Vision Perception and Control Technology School of Robotics Hunan University Changsha China College of Engineering and Design Hunan Normal University Changsha China

Domain adaptive semantic segmentation enables robust pixel- wise understanding in real-world driving scenes. Source-free domain adaptation, as a more practical technique, addresses the concerns of data privacy and storage limitations in typical unsupervised domain adaptation methods, making it especially relevant in the context of intelligent vehicles. It utilizes a well-trained source model and unlabeled target data to achieve adaptation in the target domain. However, in the absence of source data and target labels, current solutions cannot sufficiently reduce the impact of domain shift and fully leverage the information from the target data. In this paper, we propose an end-to-end source-free domain adaptation semantic segmentation method via Importance-Aware and Prototype-Contrast (IAPC) learning. The proposed IAPC framework effectively extracts domain-invariant knowledge from the well-trained source model and learns domain-specific knowledge from the unlabeled target domain. Specifically, considering the problem of domain shift in the prediction of the target domain by the source model, we put forward an importance-aware mechanism for the biased target prediction probability distribution to extract domain-invariant knowledge from the source model. We further introduce a prototype-contrast strategy, which includes a prototype-symmetric cross-entropy loss and a prototype-enhanced cross-entropy loss, to learn target intra-domain knowledge without relying on labels. A comprehensive variety of experiments on two domain adaptive semantic segmentation benchmarks demonstrates that the proposed end-to-end IAPC solution outperforms existing state-of-the-art methods. The source code is publicly available at https://***/yihong-97/Source-free-IAPC. IEEE

关键词： Semantics

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An Anonymous IoT-Based E-Health Monitoring System Using Blockchain Technology

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IEEE Systems Journal 2023年第2期17卷 2422-2433页

作者： Samuel, Omaji Omojo, Akogwu Blessing Mohsin, Syed Muhammad Tiwari, Prayag Gupta, Deepak Band, Shahab S. Department of Computer Science Osara264103 Nigeria Edo State University Uzairue300281 Nigeria Shestco Applied Mathematics and Simulation Advanced Research Centre Abuja186 Nigeria Comsats University Islamabad Department of Computer Science Islamabad45550 Pakistan Aalto University Department of Computer Science Espoo02150 Finland Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi110086 India Research Advisor Ucrd Chandigarh University GharuanPunjab Mohali140413 India National Yunlin University of Science and Technology Future Technology Research Center Douliou64002 Taiwan

The Internet of Things (IoT) has made it possible for health institutions to have remote diagnosis, reliable, preventive, and real-time decision-making. However, the anonymity and privacy of patients are not considered in IoT. Therefore, this article proposes a blockchain-based anonymous system, known as GarliMediChain, for providing anonymity and privacy during COVID-19 information sharing. In GarliMediChain, garlic routing and blockchain are integrated to provide low-latency communication, privacy, anonymity, trust, and security. Also, COVID-19 information is encrypted multiple times before transmitting to a series of nodes in the network. To ensure that COVID-19 information is successfully shared, a blockchain-based coalition system is proposed. The coalition system enables health institutions to share information while maximizing their payoffs. In addition, each institution uses the proposed fictitious play to study the strategies of others in order to update its belief by selecting the best responses from them. Furthermore, simulation results show that the proposed system is resistant to security-related attacks and is robust, efficient, and adaptive. From the results, the proposed proof-of-epidemiology-of-interest consensus protocol has 15.93% less computational cost than 26.30% of proof-of-work and 57.77% proof-of-authority consensus protocol, respectively. Nonetheless, the proposed GarliMediChain system promotes global collaborations by combining existing anonymity and trust solutions with the support of blockchain technology. © 2007-2012 IEEE.

关键词： COVID-19

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Integrating multi-modal insights with transfer learning for detecting metastatic breast cancer (MBC-stage IV) prognostics

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International Journal of Information Technology (Singapore) 2025年第1期17卷 637-643页

作者： Kaur, Sugandha Kaur, Manpreet Khanna, Ashish Department of Computer Science Mata Sundri College for Women University of Delhi New Delhi India Department of Computer Science & Technology Manav Rachna University Faridabad India Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology New Delhi India Center for Global Health Research Saveetha Medical College Saveetha Institute of Medical and Technical Sciences Chennai India

Accurate breast cancer prognosis prediction is crucial for efficient treatment planning and improving patient outcomes. While significant progress has been made in treating primary breast cancer, the development of robust predictive models remains a critical challenge. This study introduces multi-modal prognostic approach for enhanced Metastatic Breast Cancer prediction. Recognizing the limitations of relying solely on uni-modal data, our approach leverages the power of multi-modal datasets. The images are taken from breast ultrasound scans dataset and mammograms are resized into 224 × 224, and finally, data pre-processing steps are used. This study presents a two-stage model: first, a convolutional neural network is used to extract salient features from the multi-modal data. These extracted features are further concatenated for feature-fusion and served as an input for a dual transfer learning hybrid model in the second stage by combining EfficientNetB4 and InceptionV3, thus enabling robust and accurate prognosis prediction. The outputs are flattened and concatenated followed by implementation of Deep_dense layers to learn the weights of the combined models. The proposed hybrid approach effectively categorizes medical images for the prognostics of breast-cancer. Evaluations demonstrate the superior performance of our proposed model, achieving an accuracy of 99.12%, precision of 95%, recall of 93%, and F1 score of 98.12%. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Convolutional neural network (CNN) EfficientNetB Feature fusion InceptionV3 Metastatic breast-cancer Multi-modal image data

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Data Mining and Bioinformatics Approaches to Elucidate Bacterial Communities in the Extreme Environment

Data Mining and Bioinformatics Approaches to Elucidate Bacte...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Nirwantono, Rudi Asadi, Faisal Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for many industrial applications. However, the main problem is that majority of the bacteria are unculturable, hampering the exploration of bacteria from different environments. Metagenomics approach which employs Next Generation Sequencing technology could help study bacteria by utilizing 16S rRNA marker gene. This study aims to demonstrate data mining and bioinformatics approaches to analyze 16S rRNA sequencing data. The raw sequencing data of 16S rRNA was collected from biological database. Then, the data were trimmed, denoised, and clustered to generate Amplicon Sequence Variants (ASVs). Diversity (alpha and beta) and taxonomic analyses were then conducted to elucidate the bacterial diversity and taxonomic profile of ASVs. The results of this work showed that Shannon and PD indices in China's hot spring were higher than Singapore and the USA. Furthermore, a significant difference was observed in the PD index. The unweighted UniFrac distance also showed there was a significant difference of the bacterial communities between the three locations. In addition, the taxonomic investigation unveiled prevalent bacterial groups in the ecosystem, namely Proteobacteria, Chloroflexi, Cyanobacteria, and Crenarchaeota. The research outcomes have the potential to serve as a foundational resource for subsequent bacterial metagenomic research, particularly the hot spring environment. © 2023 IEEE.

关键词： Bacteria

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Knowledge Graph-Enhanced Semantic Cache for Low-Latency and Cost-Effective Inference in Large Language Models 9

Knowledge Graph-Enhanced Semantic Cache for Low-Latency and ...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Dominic, Nicholas Pardamean, Bens Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

In organizational knowledge management, Large Language Model (LLM) caches act as a semantic repository gathered from previous LLM responses. Due to intensive calls from multiple users, LLM may suffer from high inference latency. While there are many prior available approaches to solve this problem, most of them are inherently complex. This paper introduced a Knowledge Graph-enhanced Semantic Cache mechanism as an alternative, lightweight technique to boost retrieval for similar prompts. The latest state-of-the-art open-source LLM, named Google's Gemma-2B-it, was used to generate sample prompts and responses as a draft, while a knowledge graph (KG) was built from Wikipedia sentences. To create embeddings of prompts and KG, all-MiniLM-L6-v2 from SentenceTransformer was used. This new cache system resulted in up to 28% improvement over a standard model. In particular, reinforcement with KG cache embeddings yielded more than 85% semantic cache accuracy. To map the next trajectory of this pilot study, an overview of the extended framework for LLM knowledge management was also presented in this paper. The framework includes the new KG- enhanced cache system equipped with scalable security and fallback mechanisms that can promote green technology through substantial improvements in latency, throughput, and overall LLM costs. © 2024 IEEE.

关键词： Knowledge graph

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Structured Design of Multilevel Simulation Models 28

Structured Design of Multilevel Simulation Models

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28th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2024

作者： Serena, Luca Marzolla, Moreno D'angelo, Gabriele University of Bologna Department of Computer Science and Engineering Italy University of Bologna Center for Inter Department Industrial Research ICT Italy

ISBN: (纸本)9798331527211

Multilevel modeling is a design technique that allows complex models to be specified in terms of simpler ones. An obvious advantage of multilevel modeling is the possibility of reusing existing models, therefore reducing development cost. However, multilevel modeling entails more than the mere application of the ubiquitous principle of decomposition. Indeed, in a multilevel model the same entity may be described at different levels of detail during execution. This allows, for example, to focus on areas of interest by switching to a more detailed model, and revert back to a faster but less accurate one when possible. In this paper we propose GEMMA (GEneric Multilevel Modeling Abstraction), a methodology inspired by DEVS that can help the designer to produce sound and correct multilevel models. We propose a practical realization of GEMMA as a concrete software architecture that can be instantiated in different programming languages. We illustrate a simple use case where different sub-models written in different programming languages are combined according to the GEMMA methodology. Our ultimate goal is to bridge the gap between theory and practice of multilevel modeling, to foster a broader adoption of this useful technique. © 2024 IEEE.

关键词： Digital elevation model

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YOLOv8: A Novel Vehicle Mirror Detection Algorithm with Enhanced Performance using Deep Learning

YOLOv8: A Novel Vehicle Mirror Detection Algorithm with Enha...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Sundarakrishnan Narayanan Yashas Donthi Mohana Ramakanth Kumar P Sreelakshmi K Computer Science and Engineering Center for Computer Vision Research RV College of Engineering® Bengaluru India Electronics & Telecommunication Engineering Center for Computer Vision Research RV College of Engineering® Bengaluru India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

In recent times, Artificial intelligence (AI) and machine learning (ML) technologies have demonstrated remarkable capabilities in addressing real time needs and challenges across multiple domains and fields. In the world of deep learning, various Neural Network architectures like DNN, CNN, and RNN have been instrumental in advancing solutions to complex problems spanning multiple industries, business environments and law enforcement use cases. This paper implemented a special type of CNN, YOLOv8(You Only Look Once), a SOTA architecture to detect the presence of rear-view mirrors in two-wheelers for traffic surveillance and law enforcement applications. The proposed research introduces a novel idea in the realm of road safety by presenting the first ever paper on vehicle mirror detection. This approach sheds light on an often-overlooked aspect, contributing significantly to the enhancement of road safety measures. A custom dataset of 1246 training images, 155 test images and 157 validation images has been considered. This dataset has been used to train and test a binary classification model with the label "mirror". Images with the label "mirror" indicate the presence of one or both rear-view mirrors on two-wheelers. On the other hand, images with no mirrors are treated as "background" images. The model achieved a maximum precision of 93.8%, recall of 87.9%, mAP50 of 94.1% and F1-score of 90.9%. These results stand as evidence of the robustness and proficiency of the model, ensuring that it can be potentially used in real world use cases, specifically for law enforcement applications.

关键词： Deep learning YOLO Training Law enforcement Traffic control Video surveillance Road safety Robustness Mirrors Convolutional neural networks

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A review: Data pre-processing techniques used for diabetes prediction 9th

A review: Data pre-processing techniques used for diabetes p...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Cmputer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

When processing datasets in diabetes classification, common problems included a large number of missing values, outliers, and dataset imbalance. To deal with those issues, this study analyzed 18 studies on diabetes classification with machine learning algorithms over the past 5 years. This revealed the important role of data pre-processing in creating effective classification models, as it was found that by using different data pre-processing techniques, the same model can provide different performance. The study identified K-Nearest Neighbor (KNN) and support vector machine (SVM) as superior methods for filling in missing values, achieving an accuracy of 98.49% and 94.89%, respectively. These approaches outperformed traditional methods such as median or mean replacement. However, the challenge of imbalanced data sets remains in all studies reviewed. The common evaluation metrics used to evaluate the created models in previous studies included accuracy, precision, specificity, sensitivity/recall, and F1 Score. Overall, this review showed that the role of data pre-processing is no less important than algorithm selection to improve the performance of machine learning models in diabetes classification. © 2024 The Authors.

关键词： Adversarial machine learning

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Improve Harris Hawkes optimizer algorithm via Laplace crossover

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2057-2072页

作者： Nasab, Seyed Taha Mousavi Abualigah, Laith Department of Applied Mathematics Iran University of Science and Technology Tehran Iran Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Nowadays, the speed of solving optimization problems by increasing various issues and the number of variables is critical. The Harris Hawk optimization method is a brand-new, intelligent system that resolves optimization issues by mathematically simulating the natural behavior of hawks. In this study, the Harris Hawks optimization method and the Laplace crossover operator are merged, and a new enhanced Harris Hawks algorithm (LX-HHO) based on the Laplace optimization algorithm is suggested. The purpose of improving this algorithm is to increase the convergence speed while maintaining good accuracy in achieving the optimal solution. The results of the proposed method on the test functions demonstrate that the algorithm has achieved faster convergence than the original version of the Harris Hawks algorithm and other meta-heuristic algorithms. This is due to the algorithm's accuracy in obtaining the best answer in these functions and to a reduction in the evaluation of the optimization function. Also, comparing and evaluating the proposed algorithm with other algorithms by Friedman and MAE test shows the superiority of the LX-HHO algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Optimization

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