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6th International Conference on information and Communications technology, ICOIACT 2023

作者： Wirawan, Alwan Cahyono, Hasan Dwi Winarno Universitas Sebelas Maret Faculty of Information Technology and Science Data Department of Informatics Surakarta Indonesia

ISBN: (纸本)9798350315639

Instagram, a social media platform, has in the vicinity of 2 billion active users in 2023. The platform allows users to post photos and videos with one another. However, cyberbullying remains a significant problem for about 50% of young Indonesians. To address this issue, sentiment analysis for comment filtering uses a Support Vector Machine (SVM) and Easy data Augmentation (EDA). EDA will augment the dataset, enabling robust prediction and analysis of cyberbullying by introducing more variation. Based on the tests, SVM combination with EDA results in a 2.52% increase in the k-Fold Cross Validation score. Our proposed approach shows an improved accuracy of 92.5%, 2.5% higher than that of the existing state-of-the-art method. To maintain the reproducibility and replicability of this research, the source code can be accessed at ***/eda_svm. © 2023 IEEE.

关键词： Support vector machines

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Vietnamese Text Summarization Using Transformer Approach 6th

Vietnamese Text Summarization Using Transformer Approach

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Hung, Bui Thanh Tan, Phan Nguyen Trong Long, Tran Ho Phi Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh Viet Nam

ISBN: (纸本)9789819780891

Text summarization is a crucial aspect of natural language processing, finding widespread applications in various aspects of our lives. Numerous studies have delved into this task, employing diverse approaches ranging from linguistic analysis to machine learning and deep learning methodologies. However, there has been limited exploration of transformer-based models for Vietnamese text summarization. In our research, we leverage transformer models, namely, PhoBERT, BARTpho, and ViT5, to develop and refine effective summarization models. Through experimentation on the Vietnews dataset and evaluation using ROUGE metrics, our proposed method demonstrates noteworthy achievements in the realm of Vietnamese text summarization, showcasing its potential for advancing the quality of summarization tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： BARTpho BERT Deep learning PhoBERT Text summarization Transformer ViT5

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Utilization of Machine Learning in Breast Cancer Detection 25

Utilization of Machine Learning in Breast Cancer Detection

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25th International Arab Conference on information technology, ACIT 2024

作者： Momani, Mutaz Yousef, Nidal Aljawazneh, Qotadeh Issa, A.T. Abuowaida, Suhaila Alazaidah, Raed Khouj, Mohammed Talat Batyha, Radwan Elsoud, Esraa Abu Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Al-Albayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan Zarqa University Department of Data Science and Ai Faculty of Information Technology Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Private University Faculty of It Applied Science Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Breast cancer is the most prevalent cancer among women and can be deadly, necessitating early detection to enhance patient outcomes and treatment effectiveness. Recently, Machine Learning (ML) techniques have shown potential in medical diagnostics, especially in breast cancer screening. ML algorithms examine extensive medical databases, including patient records, to identify subtle patterns and anomalies indicative of cancer. These algorithms excel at recognizing intricate patterns that may be missed by human analysis. Thereby, increasing the precision of breast cancer diagnoses. By evaluating and analyzing medical data, ML algorithms can detect minute anomalies, facilitating early identification of potential cancer cases. A significant advantage of ML-based breast cancer detection is its capacity for continuous learning and adaptation. In this study, we utilized 34 classifiers and five feature selection methods on three datasets. The first dataset comprises 286 instances with 10 integer-type features. The second dataset includes 699 instances with 10 integer-type features, and the third dataset consists of 286 instances with 13 integer-type features. For dataset 1, Filtered Classifier and J-48 yielded the best results, while the Bayesnet classifier performed best on dataset 2. In dataset 3, SimpleLogistic and LMT achieved the best outcomes. Regarding feature selection, the study showed that the GainRatioAttributeEval, InfoGainAttributeEval, and CorrelationAttributeEval techniques are the best among the feature selection methods tested in the study. © 2024 IEEE.

关键词： Lung cancer

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A Novel Maize Futures Price Prediction Model based on EEMD-CNN-IGRU 22

A Novel Maize Futures Price Prediction Model based on EEMD-C...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wang, Jingyang Han, Jingyu Liu, Wei Li, Ying Sun, Qiuhong Zhou, Xiaokang Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Kansai University Faculty of Business Data Science Osaka565-0823 Japan

ISBN: (纸本)9798331509712

Maize, a significant global food crop, is essential in agriculture and the economy. The price of maize futures is affected by many factors, and its data is a nonlinear, unstable, and long-term correlation, so it is difficult to predict it accurately. Accordingly, this paper presents a combined model based on Ensemble Empirical Mode Decomposition (EEMD), Convolutional Neural Network (CNN), and Improved Gated Recurrent Unit (IGRU). EEMD decomposes the maize price data to produce multiple Intrinsic Mode Function (IMF) components and residual sequences. Subsequently, the noisy IMF components are removed, and the remaining IMF components are reconstructed into low, medium, and high frequencies. The CNN is tasked with the extraction of eigenvalues for these components. The IGRU improves in two key respects on the original Gated Recurrent Unit (GRU) model. First, it enhances the update and reset gates. Second, it incorporates the Self-Attention (SA) module. Thereby, the model's predictive capabilities are improved. This study uses the primary maize futures trading data from the Dalian Commodity Exchange as the experimental data. A comparative analysis of the EEMD-CNN-IGRU model with seven baseline models shows that it outperforms other models in all evaluation indexes. © 2024 IEEE.

关键词： Convolutional neural networks

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A Rotating Target Detection Algorithm for Ships in SAR Images Based on LAOBB-YOLOv8n 22

A Rotating Target Detection Algorithm for Ships in SAR Image...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Huang, Min Wang, Shuaihui Luo, Shuaili Sun, Qiuhong Zhou, Xiaokang Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Kansai University Faculty of Business Data Science Osaka565-0823 Japan

ISBN: (纸本)9798331509712

Aiming to address challenges in SAR image ship detection tasks, such as the dense concentration of inshore ships, small target sizes, and large overlapping areas, this paper proposes an improved algorithm LAOBB-YOLOv8n based on YOLOv8n for detecting rotating ships in SAR images. In this model, we propose the Detect-LSCD detection head, which replaces the BatchNorm in the convolutional layers of the Detect module in YOLOv8n with GroupNorm to improve the positioning and classification capabilities of the detection head;Replace the original Spatial Pyramid Pooling-Fast (SPPF) module with the Attention-based Intrascale Feature Interaction (AIFI) module, which focuses on processing advanced image features through self-attention mechanism, thus improving the flexibility and accuracy of the model in processing complex scenes while reducing unnecessary computational consumption;Based on the original network structure, we add a small target detection layer to improve the detection ability for small targets;Use rotating bounding box instead of traditional horizontal bounding box, which can better accommodate the rotating posture of the target and reduce the overlapping areas between detection boxes, thereby improving the accuracy of inshore target detection. The comparative and ablation experiments are conducted on the RSDD-SAR dataset to verify the model's effectiveness. The experimental results show that the mAP50 of LAOBB-YOLOv8n reaches 98%, which is 1.4% higher than that of YOLOv8n. Although the number of parameters of LAOBB-YOLOv8n is larger than that of YOLOv8n, it is still very small compared with other baseline models, and the accuracy of LAOBB-YOLOv8n is much higher than other models. © 2024 IEEE.

关键词： Radar imaging

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Few-shot Domain-Adaptative Visually-fused Event Detection from Text 26

Few-shot Domain-Adaptative Visually-fused Event Detection fr...

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26th International Conference on information Fusion, FUSION 2023

作者： Moghimifar, Farhad Shiri, Fatemeh Haffari, Reza Li, Yuan-Fang Nguyen, Van Monash University Faculty of Information Technology Department of Data Science and Ai Melbourne Australia Information Sciences Division Defence Science and Technology Group Adelaide Australia

ISBN: (纸本)9798890344854

Incorporating auxiliary modalities such as images into event detection models has attracted increasing interest over the last few years. The complexity of natural language in describing situations has motivated researchers to leverage the related visual context to improve event detection performance. However, current approaches in this area suffer from data scarcity, where a large amount of labelled text-image pairs are required for model training. Furthermore, limited access to the visual context at inference time negatively impacts the performance of such models, which makes them practically ineffective in real-world scenarios. In this paper, we present a novel domain-adaptive visually-fused event detection approach that can be trained on a few labelled image-text paired data points. Specifically, we introduce a visual imaginator method that synthesises images from text in the absence of visual context. Moreover, the imaginator can be customised to a specific domain. In doing so, our model can leverage the capabilities of pre-trained vision-language models and can be trained in a few-shot setting. This also allows for effective inference where only single-modality data (i.e. text) is available. The experimental evaluation on the benchmark M2E2 dataset shows that our model outperforms existing state-of-the-art models, by up to 11 points. © 2023 International Society of information Fusion.

关键词： data visualization

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Multi Label Classification Based on Ensemble Learning: Investigative Study 25

Multi Label Classification Based on Ensemble Learning: Inves...

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25th International Arab Conference on information technology, ACIT 2024

作者： Nasir, Ola Alazaidah, Raed Hadi, Wael Samara, Ghassan Al Daoud, Essam Owaida, Suhaila Abu Elrashidi, Ali Elsoud, Esraa Abu Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Petra University Faculty of Information Technology Department of Cyber Security Amman Jordan Zarqa University Faculty of Information Technolog Department of Computer Science Zarqa Jordan Al-al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Applied Science Private University Faculty of It Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

In the big data technology nowadays, activities associated with Multi-Label Classification (MLC) pose big and complex challenges, receiving terrific interest in diverse fields. Existing MLC algorithms suffer from low overall performance. This paper considers four well-known Problem Transformation Methods (PTMs). Two of these PTMs are based on the frequency of the labels (MFL and LFL) while the last two PTMs are based on the positive correlations among labels. These PTMs have been implemented and compared against four multi label datasets of various sizes to identify the best one that fits the nature of MLC datasets. Moreover, six popular ensemble learning models have been carried out and compared on the four datasets with respect to several evaluation metrics such as Accuracy, Hamming loss, and Exact Match and using SMO, DecisionStump, Random Forest, and Random Commission classifiers. The results revealed that MFL accomplished the quality performance amongst the four PTMs, and Stacking SVM is the best ensemble learning technique. © 2024 IEEE.

关键词： Contrastive Learning

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Evaluating BERT-based language models for detecting misinformation

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Neural Computing and Applications 2025年第16期37卷 9937-9968页

作者： Anggrainingsih, Rini Hassan, Ghulam Mubashar Datta, Amitava Information Technology and Data Science Faculty Sebelas Maret University Ir Sutami 36A Central Java Surakarta57126 Indonesia Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway PerthWA6009 Australia

Online misinformation poses a significant challenge due to its rapid spread and limited supervision. To address this issue, automated rumour detection techniques are essential for countering the negative impact of false information. Previous research primarily focussed on extracting text features, which proved time-consuming and less effective. In this study, we contribute substantially to two domains: rumour detection on Twitter and the evaluation of text embeddings. We thoroughly analyse rumour detection models and compare the quality of text embeddings generated by various fine-tuned BERT-based models. Our findings indicate that our proposed models outperform existing techniques. Notably, when we test these models on combined datasets, we observe significant performance improvements with larger training and testing data sizes. We conclude that carefully considering the dataset, data splitting, and classification techniques is crucial for evaluating solution performance. Additionally, we find that differences in the quality of text embeddings between RoBERTa, BERT, and DistilBERT are insignificant. This challenges existing assumptions and highlights the need for future research to explore these nuances further. © The Author(s) 2025.

关键词： BERT-based models Comparative analysis Rumour detection Text embeddings

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Enhance Efficient of Support Vector Machine with CNN and PSO for solving Handwritten Digits Recognition 28

Enhance Efficient of Support Vector Machine with CNN and PSO...

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28th International Computer science and Engineering Conference, ICSEC 2024

作者： Janprasit, Siwach Punkong, Narong Ratanavilisagul, Chiabwoot Kosolsombat, Somkiat Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand Digital Technology for Business Faculty of Management Science Kanchanaburi Thailand Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand Thammasat University Data Science and Innovation College of Interdisciplinary Studies Thailand

ISBN: (纸本)9798350366860

handwritten digit recognition is a crucial task in various fields such as postal mail sorting, bank check processing, and digitizing handwritten documents. This research aims to compare the effectiveness of using Convolutional Neural Network (CNN) for feature extraction combined with Support Vector Machine (SVM) for classification, against the traditional method of using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) for feature extraction. Additionally, Particle Swarm Optimization (PSO) is applied to optimize SVM parameters to further enhance model performance. The experiments are conducted on two datasets: The MNIST dataset and an individual handwriting dataset from 50 participants. The results indicate that the proposed method of using CNN for feature extraction and PSO-optimized SVM achieves superior accuracy compared to traditional DWT and DCT methods. Specifically, the proposed method achieved an accuracy of 99.05% on the MNIST dataset and 67.79% on the individual handwriting dataset, demonstrating its robustness and effectiveness in handwritten digit recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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Outsmarting Android Malware with Cutting-Edge Feature Engineering and Machine Learning Techniques

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Computers, Materials & Continua 2024年第4期79卷 651-673页

作者： Ahsan Wajahat Jingsha He Nafei Zhu Tariq Mahmood Tanzila Saba Amjad Rehman Khan Faten S.A.lamri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Artificial Intelligence and Data Analytics(AIDA)Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy *** escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital *** methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false *** meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in *** proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data *** Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction *** study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert *** proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,*** empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both *** app

关键词： Android malware detection machine learning SVC K-Nearest Neighbors(KNN) RF

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