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Communication & computing (WCONF), World Conference on

作者： Hanadi Alkhudhayr Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh Saudi Arabia

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Complex approaches for signal integrity analysis in high-frequency bio-sensing circuits have been developed. This article describes “Machine Learning-Enhanced Signal Integrity Analysis.” It analyses data using random forests, support vector machines, and recurrent neural networks. We compare the proposed technique to Fourier transform, Butterworth filtering, TDR, eye diagram analysis, correlation, and power spectral density to evaluate its efficacy. Precision distribution pie charts, memory distribution violin plots, F1 score distribution box plots, and accuracy comparison scatter plots can highlight the difference. These images demonstrate how successfully the recommended technique removes tiny characteristics from high-frequency bio-sensing data. The accuracy pie chart and memory violin plot demonstrate the approach's success in discovering pleasant experiences. But the false-negative rate demonstrates how effectively it finds false positives. Precision and mental balance are shown by the F1 score box plot, while the accuracy scatter plot compares all data points. Machine learning outperforms earlier approaches in accuracy, precision, memory, and F1 score. Notably, the suggested approach displays proactive error correction methods and flexibility to varying signal circumstances. It also demonstrates computational efficiency by drastically cutting down on training and inference durations in comparison to more conventional approaches. Finally, the provided machine learning-enhanced method not only improves signal integrity analysis in high-frequency bio-sensing circuits, but also establishes a new benchmark in terms of precision, robustness, and versatility. These results highlight the revolutionary potential of applying machine learning to biomedical engineering problems, opening the door to better diagnostics and real-time monitoring in settings with high-frequency signals.

关键词： Training Support vector machines Accuracy Circuits Solids Real-time systems Vectors Error correction Signal integrity Biomedical engineering

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Tailored Convolutional Neural Network for Brain Tumor Detection Using Self-attention and Spatial Attention Mechanisms

Tailored Convolutional Neural Network for Brain Tumor Detect...

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Trends in Quantum computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Raed Alsini Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Brain tumors are masses of unusual cells in the brain system and are known to be highly deadly, affecting people of all ages. Magnetic resonance imaging (MRI) is a standard approach to identifying brain malignancies. The detection of these tumors typically utilizes magnetic resonance imaging techniques. Within the field of computer vision, convolutional neural networks have become the most widely used model in the field of deep learning, especially for identifying brain tumors. This research presents a tailored CNN proposed for brain tumor detection trained on the Figshare MRI dataset. The proposed work adopts a spatial attention mechanism to find relevant spatial patterns and uses a self-attention mechanism to capture context information between the channels. Further, it implements residual connections to allow fast training and exploits a cardinality hyperparameter to reduce parameter numbers. The results assert that the proposed methodology outperforms other current methods in terms of classification performance.

关键词： Training Magnetic resonance imaging Computational modeling Training data Brain modeling Feature extraction Convolutional neural networks

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Comparative Study of Deep Learning Parameter Selection for Multi-Output Regression on Head Pose Estimation

Comparative Study of Deep Learning Parameter Selection for M...

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2022 IEEE International Conference on Industrial technology, ICIT 2022

作者： Dhanawansa, Vidushani Samarasinghe, Pradeepa Yogarajah, Pratheepan Gardiner, Bryan Karunasena, Anuradha Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka Engineering and Intelligent Systems Ulster University School of Computing United Kingdom

ISBN: (纸本)9781728119489

This paper presents a systemic analysis on the implications of the loss function, batch size and optimiser function applied within the multi-output regression problem domain of Head Pose estimation. To the best of our knowledge, prior comparative analysis of this calibre has primarily focused on the image classification problem domain, with limited analysis on the optimisation of hyperparameters influencing the performance of a Convolutional Neural Network (CNN) for regression. This motivates the current study which proposes a vanilla regression model based on the EfficientNet models of varying depths between EfficientNet BO and B5, and experiments on multiple protocols incorporating the benchmark datasets of BIWI, 300W-LP and AFLW2000. Therefore, the main contributions of the paper include a study on the impact of: loss functions for models of varying depths;batch sizes for significantly less and more complex models;and different optimiser functions on the model performance. The investigation of the effect of Mean Squared Error (MSE), Mean Absolute Error (MAE) and Huber loss functions on the performance of the models suggested that MAE and Huber loss (d=1.0) yield optimal performance regardless of the model complexity. The comparative analysis based on batch sizes ranging between 8 and 128 evidenced that moderate batch sizes of 16 and 32 yield an optimal performance on less complex models, while large batch training complements more complex models across all tested loss functions, an observation not evident in previous work. Finally, it was proven that the Adam optimiser function generates a minimal loss and is void of overfitting, outperforming other optimiser functions of Stochastic Gradient Descent, RMSProp, AdaGrad and Adam. © 2022 IEEE.

关键词： Optimization

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Comparative Study of Parameter Selection for Enhanced Edge Inference for a Multi-Output Regression model for Head Pose Estimation

Comparative Study of Parameter Selection for Enhanced Edge I...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Lindamulage, Asiri Kodagoda, Nuwan Reyal, Shyam Samarasinghe, Pradeepa Yogarajah, Pratheepan Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka School of Computing Engineering and Intelligent Systems Ulster University United Kingdom

Magnitude-based pruning is a technique used to optimise deep learning models for edge inference. We have achieved over 75% model size reduction with a higher accuracy than the original multi-output regression model fo... 详细信息

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

关键词： Deep learning

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LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection 25

LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection

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25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on Data Science and systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Qi, Jiaxing Huang, Shaohan Luan, Zhongzhi Yang, Shu Fung, Carol Yang, Hailong Qian, Depei Shang, Jing Xiao, Zhiwen Wu, Zhihui Sino-German Joint Software Institute Beihang University Beijing100191 China The Concordia Institute for Information Systems Engineering Concordia University Quebec Canada The China Mobile Information Technology Center China

ISBN: (纸本)9798350330014

The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-based anomaly detection. These methods face several challenges such as high-dimensional and noisy log data, class imbalance, generalization, and model interpretability. Recently, ChatGPT has shown promising results in various domains. However, there is still a lack of study on the application of ChatGPT for log-based anomaly detection. In this work, we proposed LogGPT, a log-based anomaly detection framework based on ChatGPT. By leveraging ChatGPT's language interpretation capabilities, LogGPT aims to explore the transferability of knowledge from large-scale corpora to log-based anomaly detection. We conduct experiments to evaluate the performance of LogGPT and compare it with three deep learning-based methods on BGL and Spirit datasets. LogGPT shows promising results and has good interpretability. This study provides preliminary insights into prompt-based models, such as ChatGPT, for the log-based anomaly detection task. © 2023 IEEE.

关键词： Anomaly detection

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CVE Records of Known Exploited Vulnerabilities 8

CVE Records of Known Exploited Vulnerabilities

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8th International Conference on Computer and Communication systems, ICCCS 2023

作者： Lim, Jackson Lau, Yi Lin Ming Chan, Leshawn Khin Tristan Paul Goo, Jia Ming Zhang, Huahua Zhang, Zhiyuan Guo, Huaqun Singapore Institute of Technology Infocomm Technology Cluster Singapore Ningbo Innovation Center Zhejiang University China NingboTech University China School of Computing and Information Systems Singapore Management University Singapore

ISBN: (纸本)9781665456128

Understanding vulnerability trends is critical to the risk management process. The goal of this paper is to raise the awareness and inform the users about the Common Vulnerabilities and Exposures (CVE) associated with Known Exploited Vulnerabilities that might affect users of the products by gathering data from the National Vulnerability Database (NVD) and CVE details from 2017 to Present. We built a system using Python and CVE Analyzer, which allow users to search for specific CVE records based on CVE ID, vendors, products, published date and the last updated date and export rows of data based on the filtered keyword. We also analyze the overall frequency of CVE records each year regarding Common Vulnerability Scoring System (CVSS) base metric trends, the vendors, and products with the most ransomware vulnerabilities. Our findings show that the frequency of all ransomware vulnerabilities increased from 2019 to 2021. Also, the number of high CVSS score of CVE records associated with ransomware is higher than those with low CVSS scores. Apple and Microsoft are the top vendors with the most exploited ransomware vulnerabilities, and IOS and Windows 10 are the products with the highest vulnerable versions by Apple and Microsoft. Our system can help users to prevent and mitigate the impact of ransomware attacks using datasets and data analysis. © 2023 IEEE.

关键词： Malware

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WISER: Weak Supervision and Supervised Representation Learning to Improve Drug Response Prediction in Cancer 41

WISER: Weak Supervision and Supervised Representation Learni...

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41st International Conference on Machine Learning, ICML 2024

作者： Shubham, Kumar Jayagopal, Aishwarya Danish, Syed Mohammed Prathosh, A.P. Rajan, Vaibhav Department of Electrical Communication Engineering Indian Institute of Science Karnataka Bengaluru India Department of Information Systems and Analytics School of Computing National University of Singapore Singapore Indian Institute of Technology Bihar Patna India

Cancer, a leading cause of death globally, occurs due to genomic changes and manifests heterogeneously across patients. To advance research on personalized treatment strategies, the effectiveness of various drugs on cells derived from cancers (‘cell lines') is experimentally determined in laboratory settings. Nevertheless, variations in the distribution of genomic data and drug responses between cell lines and humans arise due to biological and environmental differences. Moreover, while genomic profiles of many cancer patients are readily available, the scarcity of corresponding drug response data limits the ability to train machine learning models that can predict drug response in patients effectively. Recent cancer drug response prediction methods have largely followed the paradigm of unsupervised domain-invariant representation learning followed by a downstream drug response classification step. Introducing supervision in both stages is challenging due to heterogeneous patient response to drugs and limited drug response data. This paper addresses these challenges through a novel representation learning method in the first phase and weak supervision in the second. Experimental results on real patient data demonstrate the efficacy of our method WISER (Weak supervISion and supErvised Representation learning) over state-of-the-art alternatives on predicting personalized drug response. Our implementation is available at https://***/kyrs/WISER. Copyright 2024 by the author(s)

关键词： Self-supervised learning

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DeepDect: A Facial Deepfake Video Detection Application using Ensemble Learning 12

DeepDect: A Facial Deepfake Video Detection Application usin...

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12th IEEE Conference on systems, Process and Control, ICSPC 2024

作者： Tay, Wan Xin Chua, Hui Na Jasser, Muhammed Basheer Issa, Bayan Wong, Richard T.K. Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria

ISBN: (纸本)9798350391398

The emerging technology of deepfake video poses significant threats to information integrity and public trust. Deepfake videos come in various forms, including face swaps, lip-syncing, and full-body simulations. Detection algorithms may struggle to generalize across these different types of deepfakes, leading to lower effectiveness in real-world applications. An advanced deepfake detection system is developed using an ensemble approach, combining the strengths of three pre-trained Convolutional Neural Networks (CNNs)ResNeXt50, Xception, and EfficientNet-v2-with Long ShortTerm Memory (LSTM) layers to capture both spatial and temporal features from videos to improve detection accuracy. The ensemble model is further enhanced by incorporating additional features such as lip movements, eye blinks, head poses, and color contrasts to identify subtle inconsistencies in deepfake videos that are often ignored by traditional detection methods. The ensemble model achieves an accuracy of 84.06%, with a good balance between precision and recall, indicating its effectiveness in real-world applications. We developed DeepDect, a web application embedded with the ensemble model, to provide a user-friendly interface for deepfake detection by uploading videos or submitting URL links. The application includes educational content about deepfake techniques to raise user awareness about the potential dangers of deepfakes. © 2024 IEEE.

关键词： Convolutional neural networks

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Evolution of Web API Cooperation Network via Exploring Community Structure and Popularity

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6659-6671页

作者： Kang, Guosheng Wang, Yang Liu, Jianxun Cao, Buqing Xiao, Yong Xu, Yu Hunan University of Science and Technology Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China University of Cambridge Cambridge Baker Systems Genomics Initiative British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care CambridgeCB1 8RN United Kingdom

With the growing popularity of the Internet, Web applications have become increasingly essential in our daily lives. Web application programming interfaces (Web APIs) play a crucial role in facilitating interaction between applications. However, most Web service platforms are suffering from the imbalance of Web services now, many services of good quality but low popularity are difficult to be invoked even once and do not create direct connections with the users. Some graph-based Web service recommendation methods also often present a long-tailed distribution of recommended Web services due to limited Mashup-API invocation relationships. To relieve this problem and promote service recommendation, in this article, we propose a community structure and popularity-based approach by constructing an evolving cooperation network for Web APIs. We leverage the Louvain algorithm in community detection to assign community structure to each Web API and consider both the popularity and community structure in constructing the network. By optimizing the Barabαsi-Albert (BA) evolving network model, we demonstrate that our approach outperforms the BA, Bianconi-Barabαsi (BB), and popularity-similarity optimization (PSO) models in Web service clustering. Based on our proposed evolutionary network model for the evolutionary extension of API cooperation network and used for downstream Web service recommendation tasks, the experimental results also show that our recommended approach outperforms some other baseline models for Web service recommendation. © 2020 IEEE.

关键词： Web services

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