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International Conference on Informatics and computing (ICIC)

作者： Nur Ghaniaviyanto Ramadhan Gita Fadila Fitriana Data Science School of Computing Telkom University Bandung Indonesia Software Engineering Faculty of Informatics Telkom University Purwokerto Purwokerto Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

In the context of movie reviews, sentiment analysis plays a crucial role as these reviews often capture the emotions and opinions of viewers regarding the film. With the growing number of reviews across platforms like social media and movie review sites, it becomes feasible to automatically classify them into positive or negative sentiments. However, manually analyzing a dataset of 50,000 reviews is impractical. Therefore, this study focuses on conducting sentiment analysis on the IMDB movie review dataset, consisting of 50,000 entries, using an ensemble voting model. The machine learning models applied in this ensemble include Random Forest (RF) and Adaptive Boosting (AdaBoost). Two test scenarios were employed: the first compared the soft and hard voting approaches, while the second examined the impact of different training-test data splits. The findings indicate that the soft voting ensemble (SVE) model with an 80%-20% training-testing split achieved an accuracy of 88%, an F1-score of 88%, and an AUC of 0.94, which is 4% higher than the hard voting model.

关键词： Radio frequency Sentiment analysis Analytical models Adaptation models Accuracy Reviews Social networking (online) Motion pictures Ensemble learning Random forests

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Blockchain-based data Sharing Framework for Malaysia Government Aid Management System

Blockchain-based Data Sharing Framework for Malaysia Governm...

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2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023

作者： Azmi, Azman Yahya, Farashazillah Moung, Ervin Gubin Sallehudin, Hasimi Utomo, Rio Guntur Azman, Nur Afrina Universiti Malaysia Sabah Advanced Machine Intelligence Research Lab Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Universiti Malaysia Sabah Data Technologies and Application Research Lab Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Universiti Kebangsaan Malaysia Centre for Software Technology and Management Faculty of Information Science and Technology Malaysia Telkom University School of Computing Bandung Indonesia Foundation Study and Language Institute University of Reading Malaysia Johor Iskandar Puteri Malaysia

ISBN: (纸本)9798350310689

The Covid-19 pandemic has impacted the world economy, and Malaysia is no exception. Due to the closure of businesses, the rate of unemployment has been recorded at an all-time high. The Malaysian Government through its agencies is assisting citizens through an online platform for incentives and an aid-giving platform. The current architecture of data sharing is using centralized, organization-oriented authentication and is not immutable for audit capability. In addition, the blockchain network is still a proof of concept in the digital government ecosystem. This paper studies the current aid data-sharing ecosystem and analyzes the potential of having a state government consortium blockchain framework that will be used in the Government Network Infrastructure for decentralized, transparent data sharing with audit capability. Several works involving Blockchain adoption based on domain, Blockchain consortium architecture and Government applications using blockchain are being presented. The need for a framework in implementing decentralized data sharing using consortium blockchain architecture for multi-agencies data sharing focusing on Malaysia Government Aid Management is discussed in this paper to provide the opportunity for the research domain. The findings in this paper will be used for future work on the requirement of a proposed government multi-layer consortium blockchain framework for data sharing in the Malaysian Government specifically for the Johore State Government ecosystem. © 2023 IEEE.

关键词： Blockchain

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EdgeAuth: An intelligent token-based collaborative authentication scheme

EdgeAuth: An intelligent token-based collaborative authentic...

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作者： Jiang, Xutong Dou, Ruihan He, Qiang Zhang, Xuyun Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Mathematics University of Waterloo Waterloo Canada Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University China

Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply-chain supervision and collaboration, Internet of Vehicles, real-time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token-based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man-in-the-middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes. © 2023 John Wiley & Sons Ltd.

关键词： Authentication

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Enhancing data Quality in Image Pre-processing: A Case Study on Plant Disease Classification

Enhancing Data Quality in Image Pre-processing: A Case Study...

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2023 International Conference for Technological Engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Ong, Ge Ye Abdullah, Nurzulaikha Ridzuan, Fakhitah Mustafa, Wan Azani Alzubaidi, Laith H. INTI International College Penang School of Engineering and Technology Penang Malaysia Universiti Malaysia Kelantan Faculty of Data Science and Computing Kota Bharu Malaysia Universiti Malaysia Perlis Faculty of Electrical Engineering & Technology Perlis Arau02600 Malaysia The Islamic University Najaf Faculty of Engineering Iraq

ISBN: (纸本)9798350336474

data quality plays a vital role in image pre-processing for machine learning applications. The effectiveness of building accurate and reliable models lies in the high-quality of data. Prioritizing data quality in the initial stages of the image pre-processing pipeline lays a strong foundation for subsequent machine learning stages. Therefore, this research aims to identify the appropriate steps for image pre-processing and compare the performance of machine learning model based on different pre-processing approaches. In this study, we utilized a plant diseases dataset sourced from Kaggle, comprising approximately 87,000 RGB images of both healthy and diseased crop leaves, categorized into 38 distinct classes. To significantly enhance the image quality, we implemented a range of transformation techniques, including resizing, normalization, data augmentation, and cropping. The analysis clearly indicates that implementing comprehensive pre-processing techniques enhances data quality and improves the machine learning model's classification performance. © 2023 IEEE.

关键词： Machine learning

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GPU-based Parallel Technique for Solving the N-Similarity Problem in Textual data Mining 3

GPU-based Parallel Technique for Solving the N-Similarity Pr...

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3rd International Conference on Distributed computing and High Performance computing, DCHPC 2024

作者： Fazlali, Mahmood Mirhosseini, Mina Shahsavari, Mahyar Shafarenko, Alex Mashinchi, Mashallah University of Hertfordshire Cyber Security and Computing Research Group Department of Computer Science Hertfordshire United Kingdom Shahid Beheshti University Faculty of Mathematical Sciences Department of Computer and Data Sciences Tehran Iran Radboud University Donders Institute for Brain Cognition and Behaviour Ai Department Nijmegen Netherlands Shahid Bahonar University of Kerman Faculty of Mathematics and Computer Department of Statistics Kerman Iran

ISBN: (纸本)9798350381580

An important issue in data mining and information retrieval is the problem of multiple similarity or n-similarity. This problem entails finding a group of n data points with the highest similarity within a large dataset. Exact methods to solve this problem exist but come with high time and space complexities. Additionally, various metaheuristic algorithms have been proposed, including genetic algorithms, gravitational search algorithms, particle swarm optimization, imperialist competitive algorithms, and fuzzy imperialist competitive algorithms. These metaheuristics are capable of finding near-optimal solutions within a reasonable timeframe, although there is no guarantee of achieving exact results. In this paper, we employ a parallelization technique using CUDA to expedite the exact method. We conduct experiments on textual datasets to identify a group of n textual documents with the highest similarity to each other. The experimental results demonstrate that the proposed parallel exact method significantly reduces execution time compared to the best sequential approach and CPU multi-core implementation. Furthermore, it is evident that the proposed method requires less memory space than the exact method. © 2024 IEEE.

关键词： Large datasets

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Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning

arXiv

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arXiv 2024年

作者： He, Tao Wu, Tongtong Zhang, Dongyang Duan, Guiduo Qin, Ke Li, Yuan-Fang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China China Department of Data Science and AI Faculty of Information Technology Monash University Australia

Scene graph generation (SGG) endeavors to predict visual relationships between pairs of objects within an image. Prevailing SGG methods traditionally assume a one-off learning process for SGG. This conventional paradigm may necessitate repetitive training on all previously observed samples whenever new relationships emerge, mitigating the risk of forgetting previously acquired knowledge. This work seeks to address this pitfall inherent in a suite of prior relationship predictions. Motivated by the achievements of in-context learning in pretrained language models, our approach imbues the model with the capability to predict relationships and continuously acquire novel knowledge without succumbing to catastrophic forgetting. To achieve this goal, we introduce a novel and pragmatic framework for scene graph generation, namely Lifelong Scene Graph Generation (LSGG), where tasks, such as predicates, unfold in a streaming fashion. In this framework, the model is constrained to exclusive training on the present task, devoid of access to previously encountered training data, except for a limited number of exemplars, but the model is tasked with inferring all predicates it has encountered thus far. Towards LSGG, we propose to present visual content as textual representations as the input tokens for a pretrained language model, e.g., GPT-2, and develop a rehearsal strategy via an in-context prompt based on a novel knowledge-aware prompt retrieval mechanism. Rigorous experiments demonstrate the superiority of our proposed method over state-of-the-art SGG models in the context of LSGG across a diverse array of metrics. Besides, extensive experiments on the two mainstream benchmark datasets, VG and Open-Image(v6), show the superiority of our proposed model to a number of competitive SGG models in terms of continuous learning and conventional settings. Moreover, comprehensive ablation experiments demonstrate the effectiveness of each component in our model. © 2024, CC BY.

关键词： Forecasting

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Linking data separation, visual separation, and classifier performance using pseudo-labeling by contrastive learning

arXiv

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arXiv 2023年

作者： Benato, Bárbara Caroline Falcão, Alexandre Xavier Telea, Alexandru-Cristian Laboratory of Image Data Science Institute of Computing University of Campinas Campinas Brazil Department of Information and Computing Sciences Faculty of Science Utrecht University Utrecht Netherlands

Lacking supervised data is an issue while training deep neural networks (DNNs), mainly when considering medical and biological data where supervision is expensive. Recently, Embedded Pseudo-Labeling (EPL) addressed this problem by using a non-linear projection (t-SNE) from a feature space of the DNN to a 2D space, followed by semi-supervised label propagation using a connectivity-based method (OPFSemi). We argue that the performance of the final classifier depends on the data separation present in the latent space and visual separation present in the projection. We address this by first proposing to use contrastive learning to produce the latent space for EPL by two methods (SimCLR and SupCon) and by their combination, and secondly by showing, via an extensive set of experiments, the aforementioned correlations between data separation, visual separation, and classifier performance. We demonstrate our results by the classification of five real-world challenging image datasets of human intestinal parasites with only 1% supervised samples. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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Multi-Dimensional Spectrum Prediction Method Based on Efficient Adaptive Broad Learning

Multi-Dimensional Spectrum Prediction Method Based on Effici...

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IEEE Conference on Wireless Communications and Networking

作者： Niancong Ji Shufei Wang Yibin Zhang Yang Peng Tomoaki Otsuki Dusit Niyato Guan Gui College of Telecommunications and Information Engineering NJUPT Nanjing China Faculty of Science and Technology Keio University Kanagawa Japan College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Multi-dimensional spectrum prediction is essential for spectrum sharing and dynamic spectrum access (DSA), tack-ling spectrum scarcity and improving wireless communication. Traditional methods often use machine learning (ML), which requires manual feature extraction, or deep learning (DL), which demands high computational resources. This paper proposes a lightweight multi-dimensional spectrum prediction model using an adaptive broad learning network (ABLN). The model employs a sliding window to preprocess data and establishes input layers using randomly generated feature and enhancement nodes. The weights of broad learning are determined by solving the pseudo-inverse, and the structure is incrementally extended without retraining, reducing computational complexity. An adaptive node increment module optimizes hyperparameters efficiently. Experimental results demonstrate that ABLN reduces computational overhead while maintaining robust prediction performance across various scenarios.

关键词： Wireless communication Adaptation models Adaptive systems Computational modeling Dynamic spectrum access Training data Manuals Predictive models data models Robustness

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An Inconsistency-Based Hybrid Feature Selection Approach for Enhancing Medical Classification Modeling 2nd

An Inconsistency-Based Hybrid Feature Selection Approach for...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Zhao, Rong ALDharhani, Ghassan Saleh Ratnavelu, Kuru Thambiayya, Sathiyaraj Jasser, Muhammed Basheer Majeed, Anwar P. P. Abdul Luo, Yang Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Institute of Information Science and Technology Dalian University of Science and Technology Dalian China Faculty of Information Science and Technology UKM University Selangor Bangi43000 Malaysia Institute of Mathematical Sciences University of Malaya Kuala Lumpur56000 Malaysia Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur56000 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Artificial Intelligence is an essential tool for early disease recognition and supporting patient condition monitoring in the future. Timely and exact conclusions about the type of disease are significant for treatment and life extension. A combination of classification and feature selection algorithms can effectively handle complex datasets in the medical field and improve the accuracy of disease diagnosis and treatment. In this paper, we propose a new hybrid feature selection algorithm that uses inconsistency metrics as a filter method for the first step, and then feeds the resulting dataset into a wrapper method, which ultimately results in a reduced dataset. The proposed HFSIM algorithm is tested on five datasets in the medical domain from Kaggle. The obtained feature subsets are verified on three classification algorithms, KNN, LR and RF of machine learning to validate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm obtains feature subsets with low dimensionality on most of the datasets and also has high classification accuracy after testing on the classifiers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Early Diagnosis of Acute Lymphoblastic Leukemia Using YOLOv8 and YOLOv11 Deep Learning Models

arXiv

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arXiv 2024年

作者： Awad, Alaa Aly, Salah A. CSE Dept. E-Japan University of Science and Tech. Alexandria Egypt Faculty of Computing and Data Science Badya University Giza Egypt Computer Science Sect. Faculty of Science Fayoum University Fayoum Egypt

Leukemia, a severe form of blood cancer, claims thousands of lives each year. This study focuses on the detection of Acute Lymphoblastic Leukemia (ALL) using advanced image processing and deep learning techniques. By leveraging recent advancements in artificial intelligence, the research evaluates the reliability of these methods in practical, real-world scenarios. Specifically, it examines the performance of state-of-the-art YOLO models, including YOLOv8 and YOLOv11, to distinguish between malignant and benign white blood cells and accurately identify different stages of ALL, including early stages. Moreover, the models demonstrate the ability to detect hematogones, which are frequently misclassified as ALL. With accuracy rates reaching 98.8%, this study highlights the potential of these algorithms to provide robust and precise leukemia detection across diverse datasets and conditions. © 2024, CC0.

关键词： Lung cancer

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