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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Shah, Richa Bewoor, Laxmi Rak, Sarthak Patne, Ayush Vishwakarma Institute of Information Technology Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Information Technology Computer Engineering Pune India

ISBN: (纸本)9798350355611

This paper delves into the correlation between attention span and mental health. Attention span is the ability to focus on a task before being distracted by certain factors. It ranges from 2 seconds to more than 20 minutes. Meanwhile, mental health constitutes a person's psychological, sociological, and emotional well-being. Using a clustering algorithm, this research seeks to provide a concrete relation between the two across an age group of 18-25. © 2025 IEEE.

关键词： Electronic health record

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Estimation of Neurological Diseases and Age Using Brain MRI 4th

Estimation of Neurological Diseases and Age Using Brain MRI

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Ashokkumar, K. Dharshan, K. Ragupathi, T. Pritam, Anem Sagar, Amujuru Gnana Department of Computer Science Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Data Science and Business systems School of Computing SRM Institute of Science and Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9789819736898

Leveraging D-NN trained on neuroimaging data, we can effectively estimate the chronological ages of normal persons;this projected brain age has potential as a biomarker for identifying age-related disorders. The suggested technique employs a CNN cascade network from DL and the Support Vector Machine (SVM) algorithm from ML (SVM). These techniques have been employed to train MRI scans of the brain, classifying them into regular (unaffected by illness), Alzheimer’s disorder (AD), and neurocognitive disability. That we also have the ability to determine ages thanks to the age classifications on the photos. The brain MRI collection is trained using CNN and SVM, on which the classifications and age estimate will be carried out. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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A Cross-Modal Interactive Memory Network Based on Fine-Grained Medical Feature Extraction for Radiology Report Generation

A Cross-Modal Interactive Memory Network Based on Fine-Grain...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ma, Xitong Kuang, Yuansen Yuan, Lin Tian, Cheng Zeng, Yijie Liu, Song Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Radiology report generation is an essential task in the medical field, which aims to automate the generation of medical terminology descriptions of radiology images. However, this task currently suffers from several problems: 1) existing methods need to manually build knowledge graphs or templates (consuming time and effort) to introduce medical or prior knowledge to assist in report generation;2) previous models cannot handle the problem of data bias well (anomaly reports and anomaly descriptions make up only a tiny portion of the dataset), causing the models to ignore the learning of anomaly descriptions easily;3) existing approaches cannot robustly supervise the model, resulting in incomplete and inconsistent reports being generated. To address these issues, we propose a cross-modal interactive memory network based on fine-grained medical feature extraction. In our model, we design a cross-modal interactive memory network to automatically store and remember the required medical text knowledge and use this medical knowledge to help generate reports. Furthermore, we design an abnormal medical knowledge enhancement module to enhance the learning of abnormal fine-grained knowledge through the interaction of disease topics and their states to interact with text features. In addition, we design a cross-modal joint semantic loss unit to reduce semantic differences between different features and improve the visual representation ability of the model. We experimented and evaluated our model on MIMIC-CXR and IU-Xray datasets to compare with other baseline models. © 2024 IEEE.

关键词： Radiology

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TSR-Jack: An In-Browser Crypto-Jacking Detection Method Based on Time Series Representation Learning 26th

TSR-Jack: An In-Browser Crypto-Jacking Detection Method Base...

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26th International Conference on Information and Communications Security, ICICS 2024

作者： Cui, Bo Liu, Shuai Engineering Research Center of Ecological Big Data Ministry of Education Beijing China College of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)9789819788002

As the economic value of cryptocurrencies continues to ascend, an increasing number of cybercriminals exploit malicious browser scripts to commandeer the system and network resources of victims for unauthorized cryptocurrency mining. This form of browser-based mining, termed crypto-jacking, imparts substantial impact and harm to society, enterprises, and users. However, existing research methods lead to poor transferability because they require model training using labeling data from detection devices. To solve this problem, we introduce a crypto-jacking detection method based on time series representation learning, called TSR-jack. Specifically, we first collect hardware runtime system data for a period of time and generate datasets. After slicing the time series datasets, they are used to train a time series representation model, which introduces the loss of temporal information to extract temporal features. The temporal features can capture similarities between different devices and therefore address model transfer difficulties. Finally, the time vectors obtained from representation learning are fed into SVM for classification. Experimental results show that our TSR-jack method outperforms similar dynamic feature detection methods, especially when considering transferability, with a precision improvement of more than 5%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unsupervised learning

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Incentive Mechanism of Selfish Nodes Based on Energy Optimization and Game Theory

Incentive Mechanism of Selfish Nodes Based on Energy Optimiz...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Dong, XiangJia Cheng, Yuan Seah, Winston K. G. Zhang, Feng Xu, Gang College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China School of Engineering & Computer Science Victoria University of Wellington Wellington New Zealand School of Computer and Information Technology Shanxi University Taiyuan China

ISBN: (纸本)9781665410205

With the proliferation of mobile intelligent terminals, opportunistic networks have attracted widespread attention as a complementary technology to multi-network convergence. Different from traditional wireless networks, message delivery in opportunistic networks does not rely on a fixed infrastructure, but rather storing messages in a cache and utilizing the movement and encounters of nodes to relay messages. However, in practical application scenarios, nodes have limited storage space and energy and will easily exhibit selfishness. An increase in the number of selfish nodes will drastically degrade the performance of the network. To solve the problem of significant network performance degradation when the number of selfish nodes is high, this paper proposes an Incentive mechanism of Selfish nodes based on Energy optimization and Game Theory (ISEGT). The mechanism abstracts the process of forwarding messages by nodes into a bargaining game process, and selectively forwards messages based on nodes' remaining energy and other circumstances. The experimental results show that the ISEGT mechanism can motivate selfish nodes to actively participate in message forwarding, which improves the success rate of message delivery and the survival rate of nodes, and optimizes the overall performance of the network. © 2024 IEEE.

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FL@FM-TheWebConf’24: International Workshop on Federated Foundation Models for the Web 24

FL@FM-TheWebConf’24: International Workshop on Federated Fo...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： King, Irwin Long, Guodong Xu, Zenglin Yu, Han Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Faculty of Engineering and IT University of Technology Sydney Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400701726

In the evolving landscape of machine learning research, two significant developments have emerged to prominence: foundation models and federated learning. The FL@FM-TheWebConf’24 workshop provides an exciting forum for researchers and practitioners to discuss promising works and chart potential trajectories forward for this emerging field. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Frequency modulation

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IMF: Interactive Multimodal Fusion Model for Link Prediction 23

IMF: Interactive Multimodal Fusion Model for Link Prediction

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2023 World Wide Web Conference, WWW 2023

作者： Li, Xinhang Zhao, Xiangyu Xu, Jiaxing Zhang, Yong Xing, Chunxiao Department of Computer Science and Technology Tsinghua University Beijing China School of Data Science City University of Hong Kong Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781450394161

Link prediction aims to identify potential missing triples in knowledge graphs. To get better results, some recent studies have introduced multimodal information to link prediction. However, these methods utilize multimodal information separately and neglect the complicated interaction between different modalities. In this paper, we aim at better modeling the inter-modality information and thus introduce a novel Interactive Multimodal Fusion (IMF) model to integrate knowledge from different modalities. To this end, we propose a two-stage multimodal fusion framework to preserve modality-specific knowledge as well as take advantage of the complementarity between different modalities. Instead of directly projecting different modalities into a unified space, our multimodal fusion module limits the representations of different modalities independent while leverages bilinear pooling for fusion and incorporates contrastive learning as additional constraints. Furthermore, the decision fusion module delivers the learned weighted average over the predictions of all modalities to better incorporate the complementarity of different modalities. Our approach has been demonstrated to be effective through empirical evaluations on several real-world datasets. The implementation code is available online at https://***/HestiaSky/IMF-Pytorch. © 2023 Owner/Author.

关键词： Knowledge graph

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Real-Time Road Damage Detection and Infrastructure Evaluation Leveraging Unmanned Aerial Vehicles and Tiny Machine Learning

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IEEE Internet of Things Journal 2024年第12期11卷 21347-21358页

作者： Khan, Muhammad Waseem Obaidat, Mohammad S. Mahmood, Khalid Batool, Dania Badar, Hafiz Muhammad Sanaullah Aamir, Muhammad Gao, Wu Comsats University Islamabad Sahiwal Campus Department Of Computer Science Sahiwal57000 Pakistan Superior University Department Of Computer Science Lahore54000 Pakistan The University Of Jordan King Abdullah Ii School Of Information Technology Amman11942 Jordan University Of Science And Technology Beijing School Of Communication And Computing Engineering Beijing100083 China Amity University Amity School Of Engineering And Technology Uttar Pradesh Noida201301 India National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan University Of Palermo Department Of Engineering Palermo90133 Italy Emerson University Department Of Emerging Computing Technologies Multan60000 Pakistan Northwestern Polytechnical University Center For Bio-Inspired System-on-Chip School Of Computer Science Xi'an710060 China

Road damage detection (RDD) through computer vision and deep learning techniques can ensure the safety of vehicles and humans on the roads. Integrating unmanned aerial vehicles (UAVs) in RDD and infrastructure evaluation (IE) has also emerged as a key enabler, contributing significantly to data acquisition and real-time monitoring of road damages, such as potholes, cracks, and surface anomalies, facilitating proactive maintenance and improved road conditions. These UAVs are low-powered and resource-constrained devices that work autonomously to perform pattern detection and decision making leveraging tiny machine learning (Tiny ML) algorithms. These Tiny ML algorithms are designed to run on edge devices, IoT devices, UAVs, etc. In this study, the RDD2022 data set collected using UAVs and dashboard cameras of vehicles was utilized to train pure and mixed models that exhibit class instance imbalance in certain classes which is addressed by implementing data augmentation as a regularization technique. State-of-the-art two-stage detectors: Faster R-CNN ResNet101 and one-stage detectors: SSD MobileNet V1 FPN, YOLOv5, and Efficientdet D1 are employed. The results indicate that the two-stage detector achieved an impressive mean average precision (mAP) of 88.49% overall and 96.62% for focused classes. Notably, the state-of-the-art Efficientdet D1 approach achieved a competitive mAP of 86.47% overall and 95.12% for focused classes, with significantly lower computational cost. These findings highlight the potential of advanced object detection techniques, particularly Efficientdet D1, to enhance the accuracy and efficiency of RDD systems, thereby improving passenger safety and overall performance. © 2014 IEEE.

关键词： Object detection

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The Impact of Stuttering Event Representation on Detection Performance 2

The Impact of Stuttering Event Representation on Detection P...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Banna, Abedal-Kareem Fang, Hui Shubita, Ahmad F. Arafah, Mohammad Eqteeshat, Amani Abuaisheh, Saif B. University Of Petra Department Of Ai And Data Science Jordan Loughborough University Department Of Computer Science United Kingdom University Of Petra Department Of Software Engineering Jordan University Of Petra Department Of Computer Science Jordan Malaya University Department Of Information System Malaysia

ISBN: (纸本)9798350394962

Stuttering or stammering is a common neurogenic, psychogenic fluency disorder wherein people have uncontrolled blocks on the natural flow of speech. Automatic Stuttering Event Detection (SED) is a widely recognised challenge due to the heterogenic and overlapped nature of stuttering speech. Therefore, this comparative study investigates the impact of stuttering event representation on detection performance. The paper evaluates the performance of SED and shows the impact of employing ASR pre-trained feature extractions on each stuttering event. Moreover, the paper provides different groups of experiments that evaluate the impact of three acoustic features, the Zero Crossing Rate (ZCR), the Spectral Flux Onset Strength Envelope (SFO), and Fundamental Frequency (FF) features, on SED performance. The paper demonstrates that the fusion of spectral and ASR features improves SED performance across all stuttering events by 8%. The F1 score of the Block class increased by approximately 10%, from 15% to 25%, while the word repetition and interjection increased by 8%. In addition, our experiments proved that the fusion of temporal features increased the average F1 score of SED by 2% across all stuttering events. The F1 score of the prolongation and sound repetition classes increased by 6% and 3%, respectively. © 2024 IEEE.

关键词： Deep learning

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Electronic structure prediction of multimillion atom systems through uncertainty quantification enabled transfer learning

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npj Computational Materials 2024年第1期10卷 1423-1437页

作者： Shashank Pathrudkar Ponkrshnan Thiagarajan Shivang Agarwal Amartya S.Banerjee Susanta Ghosh Department of Mechanical Engineering-Engineering Mechanics Michigan Technological UniversityHoughtonMIUSA Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA90095USA Department of Materials Science and Engineering University of CaliforniaLos AngelesCA90095USA Faculty member of the Center for Data Sciences Michigan Technological UniversityHoughtonMIUSA

The ground state electron density—obtainable using Kohn-Sham Density Functional Theory(KSDFT)simulations—contains a wealth of material information,making its prediction via machine learning(ML)models ***,the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation,making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system ***,we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data,while comprehensively sampling systemconfigurations using *** ML models are less reliant on heuristics,and being based on Bayesian neural networks,enable uncertainty *** show that our models incur significantly lower data generation costs while allowing confident—and when verifiable,accurate—predictions for a wide variety of bulk systems well beyond training,including systems with defects,different alloy compositions,and at multi-million-atom ***,such predictions can be carried out using only modest computational resources.

关键词： alloy prediction transfer

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