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International Conference on Advanced engineering, Technology and Applications, ICAETA 2024

作者： Agrawal, Atul Khanday, Akib MohiUd Din Alazzawi, Esraa Mohammed Bhushan, Bharat Baniya, Pashupati Jamil, Akhtar Department of Computer Science and Engineering ITS Engineering College Greater Noida India Department of Computer Science and Engineering Sharda University Greater Noida India Department of Computer Science and Software Engineering United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Department of Computer Engineering University of Baghdad Al-Jadriya Baghdad10071 Iraq Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

ISBN: (纸本)9783031709234

Breast cancer continues to be a significant global health issue that greatly affects the well-being of people worldwide. Detecting breast cancer early is vital for improving the outcomes of patients. One promising method for breast cancer detection is the use of the Random Forest machine learning algorithm. In a recent research paper, we investigated how Random Forest can be used to predict breast cancer by employing explainable AI techniques. We analyzed the specific features that the algorithm relies on to classify breast cancer and highlighted the advantages of Random Forest compared to other machine learning algorithms in diagnosing breast cancer. Our model offers a well-thought-out, efficient, and easily understandable approach to predicting breast cancer using explainable machine learning techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Heart rate variability analysis in controls and epilepsy patients with or without receiving treatment: a clinical review and meta-analysis

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Neural Computing and Applications 2024年第29期36卷 18043-18063页

作者： Shahnawaz, Muhammad Bilal Dawooda, Hassan Iqbal, Uzair Department of Information Engineering Technology National Skills University Islamabad Islamabad4400 Pakistan Software Engineering Department University of Engineering and Technology Taxila Pakistan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

The malfunctioning of cardiac autonomic control in epileptic patients develops ventricular tachyarrhythmia and causes sudden unexpected death in epilepsy patients (SUDEP). Various clinical studies investigated the effect of epilepsy on cardiac autonomic control by performing heart rate variability (HRV) analysis;however, results are unclear regarding whether sympathetic, parasympathetic, or both branches of the autonomic nervous system (ANS) are affected in epilepsy and also the impact of anticonvulsant treatment on the ANS. This study follows the systematic protocols to investigate epilepsy and its anticonvulsant treatment on cardiac autonomic control by using linear and nonlinear HRV analysis measures. The electronic databases of PubMed, Embase, and Cochrane Library were used for the collection of studies. Initially, 1475 articles were identified whereas after 2-staged exclusion criteria, 33 studies were selected for execution of the review process and meta-analysis. For meta-analysis, four comparisons were performed (epilepsy patients): (1) controls (healthy subject with no history of epilepsy) versus untreated patients;(2) treated (patients under treatment that have a seizure) versus untreated patients;(3) controls versus treated patients;and (4) refractory versus well-controlled (epilepsy patients that were seizure-free for last 1 year). For treated and untreated patients, there was no significant difference whereas well-controlled patients presented higher values as compared to refractory patients. Meta-analysis was performed for the time-domain, frequency-domain, and nonlinear parameters. Untreated patients in comparison with controls presented significantly lower HF (high-frequency) and LF (low-frequency) values. These LF (g = − 0.9;95% CI − 1.48 to − 0.37) and HF (g = − 0.69;95% confidence interval (CI) − 1.24 to − 0.16) values were affirming suppressed both, vagal and sympathetic activity, respectively. Additionally, LF and HF value was increased in most o

关键词： Heart

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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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Migrant Resettlement by Evolutionary Multiobjective Optimization

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 51-65页

作者： Liu, Dan-Xuan Gu, Yu-Ran Qian, Chao Mu, Xin Tang, Ke Nanjing University National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing210023 China Peng Cheng Laboratory Shenzhen518000 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

Migration has been a universal phenomenon, which brings opportunities as well as challenges for global development. As the number of migrants (e.g., refugees) increases rapidly, a key challenge faced by each country is the problem of migrant resettlement. This problem has attracted scientific research attention, from the perspective of maximizing the employment rate. Previous works mainly formulated migrant resettlement as an approximately submodular optimization problem subject to multiple matroid constraints and employed the greedy algorithm, whose performance, however, may be limited due to its greedy nature. In this article, we propose a new framework called migrant resettlement by evolutionary multiobjective optimization (MR-EMO), which reformulates migrant resettlement as a biobjective optimization problem that maximizes the expected number of employed migrants and minimizes the number of dispatched migrants simultaneously, and employs a multiobjective evolutionary algorithm (MOEA) to solve the biobjective problem. We implement MR-EMO using three MOEAs: the popular nondominated sorting genetic algorithm II (NSGA-II), MOEA based on decomposition (MOEA/D) as well as the theoretically grounded global simple evolutionary multiobjective optimizer (GSEMO). To further improve the performance of MR-EMO, we propose a specific MOEA, called GSEMO using matrix-swap mutation and repair mechanism (GSEMO-SR), which has a better ability to search for feasible solutions. We prove that MR-EMO using either GSEMO or GSEMO-SR can achieve better theoretical guarantees than the previous greedy algorithm. Experimental results under the interview and coordination migration models clearly show the superiority of MR-EMO (with either NSGA-II, MOEA/D, GSEMO or GSEMO-SR) over previous algorithms, and that using GSEMO-SR leads to the best performance of MR-EMO. © 2024 IEEE.

关键词： Multiobjective optimization

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Enhanced Loan Approval Prediction System using Ensemble Machine Learning Techniques 3

Enhanced Loan Approval Prediction System using Ensemble Mach...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Gothai, E. Rajalaxmi, R.R. Thamilselvan, R. Sridhar, K. Department of Computer Technology Kongu Engineering College Tamilnadu Perundurai India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Perundurai India MSc Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798331532420

Banks play a pivotal role in generating significant profits through loan operations. However, the challenge lies in accurately identifying genuine loan applicants who are likely to repay their loans. Manual processes are prone to misconceptions, prompting the development of a loan prediction system using deep learning. This system aims to automate the selection of eligible candidates, benefiting both bank staff and applicants by drastically reducing the loan approval timeframe. Two key machine learning algorithms such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are employed in this work for applicant selection. SVM, a popular supervised learning algorithm, creates a decision boundary or hyperplane to segregate classes in n-dimensional space. Conversely, ANN is inspired by biological neural networks, which mimic the organization of neurons in the input layer, hidden and output layers of the human brain. The integration of ANN and SVM in an ensemble model enhances accuracy compared to traditional methods. This collaborative approach ensures precise predictions of loan repayment likelihood based on applicant data and past loan eligibility records. The model's performance is measured using accuracy. The outcomes of the research were promising and the loan prediction system defines the minimum and maximum loan amounts that can be sanctioned to an applicant. By leveraging the strengths of both ANN and SVM, the ensemble model serves as a robust tool for accurately identifying worthy loan applicants, streamlining the loan approval process, and minimizing the risk of default. The accuracy of the algorithm is improved by this method to 91%. © 2024 IEEE.

关键词： Support vector machines

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Parkinson’s Disease Detection from Resting State EEG Using Multi-head Graph Structure Learning with Gradient Weighted Graph Attention Explanations 7th

Parkinson’s Disease Detection from Resting State EEG Using...

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7th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9783031787607

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Empowering Cross-Device Data Security Verification for IoT Sensor Nodes 7

Empowering Cross-Device Data Security Verification for IoT S...

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7th International Conference on Machine Learning and Natural Language Processing, MLNLP 2024

作者： Shi, Xin He, Sheng Wang, Ting Zhang, Chong Wang, Yang Zhang, Xiao School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China School of Information Engineering Mianyang Teachers College Mianyang China

ISBN: (纸本)9798350354973

In the process of realizing the Internet of Everything through the Internet of Things (IoT), the massive deployment of sensor nodes poses significant security challenges. This paper introduces a novel approach to anomaly detection. The proposed method focuses on generating secure replicas of sensor data, which are compared with real-time data to identify anomalies. This process ensures robust detection across multiple devices and terminals, a key challenge in the realization of the Internet of Everything. The model combines Long Short-Term Memory (LSTM) networks and Variational Autoencoders (VAE), leveraging their strengths in capturing temporal dependencies and learning latent feature representations from sensor data. To enhance detection accuracy in complex environments, an adaptive threshold algorithm is employed, dynamically adjusting thresholds based on real-time data's statistical properties. A crossattention mechanism is also integrated, allowing the model to effectively capture spatio-temporal relationships between sensors. Additionally, we employed a weighted smoothing algorithm to reduce noise while preserving data integrity, ensuring accurate anomaly detection even in noisy environments. Finally, we evaluated our method using the real-world dataset of the Belgian railway bridge KW51. The results indicate that our system can generate sensor data replicas with 99.02% accuracy and detect abnormal data with 99.8% precision. Based on these findings, we believe that this novel approach can effectively perform anomaly detection and provide robust support for IoT security across multiple terminals and devices. © 2024 IEEE.

关键词： Sensor nodes

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Forecasting Price Prediction for Vegetables and Fruits Using Recurrent Neural Network 5

Forecasting Price Prediction for Vegetables and Fruits Using...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Gothai, E. Rajalaxmi, R.R. Thamilselvan, R. Harshath, S.M. Kongu Engineering College Department of Computer Technology Tamilnadu Perundurai India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu Perundurai India Kongu Engineering College MSc Software Systems Erode India

ISBN: (纸本)9798350379945

Unpredictable fruit and vegetable prices create significant challenges for farmer livelihoods. This research proposes an innovative approach using recurrent neural networks (RNNs) to predict both minimum and maximum prices. By analyzing historical market data, these models can forecast potential price ranges, empowering farmers to make informed decisions about planting, harvesting, and sales strategies. This enhanced foresight helps farmers navigate market volatility and improve income stability. © 2024 IEEE.

关键词： Vegetables

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TiTDet: A tiny text detector with scale-sensitive loss and effective fusion factor

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Journal of Intelligent and Fuzzy Systems 2024年第1期1卷 11367页

作者： Wei, Guangcun Fu, Jihua Pan, Zhifei Fang, Qingge Zhang, Zhi College of Intelligent Equipment Shandong University of Science and Technology Shandong Taian China College of Computer Sicence and Engineering Shandong University of Science and Technology Shandong Qingdao China Taian Synergy Software Ltd. Shandong Taian China

The text in natural scenes is often smaller compared to artificially designed text. Due to the small proportion of pixels, low resolution, less semantic information, and susceptibility to complex scenes, tiny text detection often results in many missed detections. To address this issue, this paper draws inspiration from small object detection methods and proposes TiTDet, a detection algorithm more suitable for tiny text. Due to the small proportion of pixels, low resolution, less semantic information, and susceptibility to complex scenes, tiny text detection often results in many missed detections. To address this issue, this paper draws inspiration from small object detection methods and proposes TiTDet, a detection algorithm more suitable for tiny text. Firstly, this paper incorporates a context extraction module and an attention-guided module. These modules guide contextual information learning through a self attention mechanism, while eliminating the possible negative impact caused by redundant information. Regarding multi-scale feature fusion, this paper proposes a fine-grained effective fusion factor, making the fusion process emphasize small object learning more and highlight the feature expression of tiny texts. In terms of post-processing, this paper proposes a differentiable binarization module, incorporating the binarization process into model training. Leveraging the implicit information in the data to drive model improvement can enhance the post-processing effect. Lastly, this paper proposes a scale-sensitive loss, which can handle tiny texts more fairly, fully considering the positional relationship between the predicted and real regions, and better guiding the model training. This paper proves that TiTDet exhibits high sensitivity and accuracy in detecting tiny texts, achieving an 86.0% F1-score on ICDAR2015. The paper also compares the superiority of the method on CTW1500 and Total-Text. © 2024 - IOS Press. All rights reserved.

关键词： Pixels

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AI-Enhanced AUTOSAR Configuration: Efficient Methods for Dataset Generation and Automated Code Production 8

AI-Enhanced AUTOSAR Configuration: Efficient Methods for Dat...

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8th IEEE International Forum on Research and Technologies for Society and Industry Innovation, RTSI 2024

作者： El-Gnainy, Nada Shanouda, Mira Essam, Ahmed Ibrahim, Anas Abdallah William, John Elsharkawy, Mariam Moustafa, Passant Ahmed Al Ansary, Mohamed Mahmoud, Hossam Moro, Ahmed Salama, Cherif Siemens Digital Industries Software Cairo Egypt The American University in Cairo Department of Computer Science and Engineering Egypt Ain Shams University Department of Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9798350362138

This research presents a novel approach to stream-line the configuration of AUTOSAR (Automotive Open System Architecture) modules using Artificial Intelligence (AI)-based tools. Traditional methods of generating AUTOSAR-compliant ARXML files are time-consuming and require meticulous man-ual input, posing significant challenges for developers. In this study, an AI-driven methodology is introduced to address these challenges. By mapping English descriptions to YAML through a Python script and subsequently converting YAML to Autosar XML (ARXML), this approach conserves tokens and significantly accelerates the AI training process. The AI model, trained on a dataset of 12,000 samples and tested on 3,000 unseen inputs, achieved a 100% accuracy rate in converting descriptions. Additionally, a comprehensive configurator tool and a code generator were developed to facilitate seamless integration and deployment of the generated configurations. The proposed solution not only reduces manual effort but also increases reliability in AUTOSAR configuration, offering a practical tool for developers in the automotive software industry. © 2024 IEEE.

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