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Scalable Machine learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios 24

Scalable Machine Learning Architectures for IPA-Driven Maint...

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7th international conference on Information Science and Systems, ICISS 2024

作者： Huang, Zhimeng Liu, Xiaodong Romdhani, Imed Shih, Chi-Sheng School of Computing Engineering and Built Environment Edinburgh Napier University Edinburgh United Kingdom College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400717567

this research presents a groundbreaking approach to Building Maintenance Management (BMM) by introducing an intelligent Process Automation (IPA)-Driven Building Maintenance Management (IBMM) model. this innovative model harnesses the synergies between Artificial Intelligence (AI), Machine learning (ML), and Internet of things (IoT) technologies to transition from reactive to proactive and predictive building maintenance strategies. the study highlights the critical gap in current BMM practices—the absence of intelligent systems for anticipating and addressing maintenance issues before they escalate. through an extensive literature review, the transformative potential of AI and IoT for enhancing building maintenance management within smart cities is explored, establishing a foundation for the IBMM model’s application. the core of this research lies in its novel application of scalable machine learning architectures to automate and optimize maintenance task allocation in large-scale building portfolios. the practicality of the IBMM model is demonstrated via a proof of concept (POC) in an industrial setting, evidencing its capacity to improve efficiency, reduce costs, and bolster sustainability in building maintenance operations. the model epitomizes a paradigm shift in BMM by integrating IPA, which combines AI and ML, facilitating automated, intelligent decision-making and task allocation. Among its advancements, the IBMM model introduces enhanced predictive maintenance through real-time data analysis, adaptive learning and optimization, automated decision-making, and human-machine collaboration, contributing to energy efficiency and alignment with smart city objectives. the paper delineates the methodology, design, and implementation of a machine learning model for engineer task assignments, culminating in a case study that validates the model’s efficacy. this research not only signifies a significant advancement in BMM by leveraging IPA technologies for autonomous p

关键词： Predictive maintenance

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Application and Development of intelligent Systems in Monitoring and Early Warning of Coastal Soft Soil Foundation 4

Application and Development of Intelligent Systems in Monito...

引用

4th international conference on Machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Wang, Luyao Qiao, Xiaoli Li, Mingyu Zhang, Yong Experiment & Inspection Center for Water Transport Engineering Tianjin Research Institute for Water Transport Engineering M.O.T. Tianjin300456 China

In coastal areas, due to their special geological conditions, soft soil foundation has become a common form of foundation in engineering construction. Soft soil foundation has low bearing capacity and large settlement deformation, posing a serious threat to surrounding building facilities and human life and property. therefore, monitoring it is crucial. However, the inherent characteristics of soft soil foundation make it difficult to monitor, and traditional monitoring methods have shortcomings such as poor timeliness, low manual efficiency, and difficulty in sharing monitoring data. therefore, in practical engineering, a combination of manual and automated methods is often used for monitoring. With the advancement of technology and the development of new technologies such as artificial intelligence, applying intelligent systems to soft soil foundation monitoring, real-time monitoring and early warning have become a new research direction. this article took a coastal soft soil foundation project as an example to introduce the application of intelligent systems in this project. © 2024 the Authors.

关键词： intelligent systems

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Fusion of Computer Vision and Natural Language Processing: automated Image Caption Generation 4

Fusion of Computer Vision and Natural Language Processing: A...

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4th international conference on intelligent Technologies, CONIT 2024

作者： Akku, Chandra Prakash Cheemalwar, Srushith Chamakura, Vasanth Gogu, Shiva Shankar Swaraja, K. Arun Vignesh, N. Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350349900

In this work the advancement in the field of natural language processing and computer vision, introduces a robust image captioning model that harnesses the capabilities of attention mechanisms. the model encompasses several core components, including the loading and preprocessing of datasets and the integration of MobileNetV3small. the model architecture is designed with a multi-layer transformer decoder, indicating self-attention, and cross-attention mechanisms for a deeper understanding of the image and text data. Training this model involves fine-tuning MobileNetV3small and optimizing the transformer-based decoder for accurate and context-aware caption generation. the utilization of MobileNetV3small and the Transformer-based decoder marks a significant advancement in image captioning, and the model holds promise for applications in automated image description and content generation. the model was tested on diverse datasets, as exemplified by its performance on both the Flicker8k and Conceptual Captions datasets. the training process involves a comprehensive pipeline, encompassing dataset preparation, image feature extraction, and tokenization, ensuring a holistic approach to model learning. the comprehensive result analysis highlighted the enhanced captioning results of the proposed method with CIDEr values of 37.20 and 25.40 on the Flickr8k dataset and conceptual captions dataset. © 2024 IEEE.

关键词： Decoding

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automated Classification of Medical Images Using Convolutional Neural Networks 15

Automated Classification of Medical Images Using Convolution...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pillai, N. Muthuvairavan Manimala, S. Rongali, Aruna Sri Gugnani, Anchit Kumar, Senthil A. Sriramakrishnan, G.V. Computer Science and Business Systems R.M.D Engineering College Thiruvallur district Kavarapettai India Computer science and engineering University SJCE JSS Science and technology University Karnataka Mysore India Sr. Software Engineer Department Information Technology Emphasis/Information Technology Ph.D University The country University of The Cumberlands United States Physiotherapy Amity University Rajasthan Jaipur India Artificial Intelligent department Shri Vishnu Engineering College for Women AP Bhimavaram India Tamilnadu Chennai India

ISBN: (纸本)9798350370249

Medical imagery has been increasingly used in diagnosing and treating various diseases, leading to advances in healthcare. While humans usually lead the qualitative interpretation of visual features, automated Classification of Medical Images Using Convolution Neural Networks (CNNs) has gained traction for accurate image classification. CNNs are a type of deep learning algorithm that attempts to mimic the structure of the human brain and are ideal for image classification due to their convolution layers that can detect patterns within the image. these convolution layers enable the networks to learn the characteristics of the data through a series of convolution and pooling operations. Additionally, they can rapidly identify relationships and patterns within the dataset and extract features from the input image, thus allowing a more accurate classification. With the increasing availability of medical imaging data, advancements in the field of ML/AI has enabled the use of CNNs for automated medical image classification, helping clinicians to diagnose diseases accurately with less time and effort. ©2024 IEEE.

关键词： Convolutional neural networks

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Industrial Object Detection: Leveraging Synthetic data for Training Deep learning Models 11th

Industrial Object Detection: Leveraging Synthetic Data for T...

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11th international conference on Industrial engineering and Applications - Europe (ICIEA - Europe)

作者： Ouarab, Sarah Boutteau, Remi Romeo, Katerine Lecomte, Christele Laignel, Aristid Ragot, Nicolas Duval, Fabrice Univ Rouen Normandie Univ Le Havre Normandie Normandie Univ INSA Rouen NormandieLITIS UR 4108 F-76000 Rouen France LINEACT CESI Rouen France LINEACT CESI Lyon France

ISBN: (纸本)9783031581120;9783031581137

the increasing use of synthetic training data has emerged as a promising solution in various domains due to its ability to provide accurately labelled datasets at a lower cost compared to manually annotated real-world data. In this study, we investigate the use of synthetic data for training deep learning models in the field of industrial object recognition. Our goal is to evaluate the performance of different models trained with varying ratios of real and synthetic data, with the aim of identifying the optimal ratio that yields superior results. In addition, we investigate the impact of introducing randomisation into the synthetic data on the overall performance of the trained models. the results of our research contribute to the understanding of the role of synthetic data in industrial object detection.

关键词： Synthetic data Deep learning Detection models Industrial Objects

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automated Penetration Testing Based on MATD Algorithm 4

Automated Penetration Testing Based on MATD Algorithm

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4th international conference on Artificial Intelligence, Virtual Reality and Visualization, AIVRV 2024

作者： Ren, Qiankun Xiong, Xinli Liu, Jingju Wang, Zhen National University of Defense Technology College of Electronic Engineering Hefei China

ISBN: (纸本)9798331528744

With the rapid advancement of internet technology, cybersecurity risks are continuously increasing, and malicious cyberattacks have caused significant losses to users, making it crucial to maintain network security. automated penetration testing plays a vital role in current cybersecurity efforts. However, existing automated penetration testing methods suffer from inefficiencies in information utilization, inaccurate action decision-making, and an overly large action space. To address these issues, we propose a new algorithm called Multi-Agent Task Decomposition(MATD), based on historical experience information and task decomposition. MATD enhances the efficiency of utilizing historical observation data through the use of Long Short-Term Memory(LSTM) networks and reduces the action space of individual agents by employing a multi-task decomposition method. Experimental results demonstrate that the proposed method outperforms the traditional Proximal Policy Optimization(PPO) algorithm by achieving higher cumulative rewards in fewer steps, thereby improving decision-making in intelligent penetration testing. this approach holds promise for increasing the efficiency of penetration testing and enhancing overall network security. © 2024 IEEE.

关键词： Reinforcement learning

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BIOGAN-BERT: BioGPT-2 Fine Tuned and GAN-BERT for Extracting Drug Interaction Based on Biomedical Texts 11

BIOGAN-BERT: BioGPT-2 Fine Tuned and GAN-BERT for Extracting...

引用

11th international conference on Advanced Informatics: Concept, theory and Application, ICAICTA 2024

作者： Parameswara, Made Arbi Mandala, Rila School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798331520311

Drug-drug interactions (DDIs) occur when two or more drugs are used together, leading to unexpected and potentially harmful effects. Identifying DDIs requires manual annotations, but the increasing volume of research publications and the slow data annotation process make this challenging. Machine learning, especially deep learning, can efficiently extract and identify DDIs from biomedical literature. However, class imbalance in datasets reduces model performance. this study introduces BIOGAN-BERT, which combines data augmentation using the Pretrained Language Model (PLM) BioGPT-2 and Generative Adversarial Network (GAN) to address class im-balance in DDI extraction tasks. It identifies gaps in existing imbalance handling studies and proposes enhancements through PLM-based data augmentation and semi-supervised learning with GAN. BioGPT-2 generates additional data from labeled and unlabeled sources, enriching the training dataset. this data is then processed using GAN-BERT, allowing the model to learn from more complex data distributions, thereby improving data quality and model generalization. Traditional methods like sampling only increase the number of data instances, and loss functions merely assign greater representation to the loss values. While these methods expand the learning space for models, they do not enhance data representation. In contrast, this novel approach uses data augmentation to increase both the quantity and the diversity of data. Evaluation results show that BIOGAN-BERT outperforms several baselines, significantly increasing the micro F1-Score for minor classes to 0.85 compared to 0.83 for the best baseline model, demonstrating its effectiveness in handling class imbalance and contextual variations in biomedical data. © 2024 IEEE.

关键词： Generative adversarial networks

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Mutual Cross-Attention in Dyadic Fusion Networks for Audio-Video Emotion Recognition 11

Mutual Cross-Attention in Dyadic Fusion Networks for Audio-V...

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11th international conference on Affective Computing and intelligent Interaction (ACIIW)

作者： Luo, Jiachen Phan, Huy Wang, Lin Reiss, Joshua Queen Mary Univ London Ctr Digital Mus London England Amazon Alexa Cambridge MA USA

ISBN: (纸本)9798350327458

Multimodal emotion recognition is a challenging problem in the research fields of human-computer interaction and pattern recognition. How to efficiently find a common sub-space among the heterogeneous multimodal data is still an open problem for audio-video emotion recognition. In this work, we propose an attentive audio-video fusion network in an emotional dialogue system to learn attentive contextual dependency, speaker information, and the interaction of audio-video modalities. We employ pre-trained models, wav2vec, and Distract your Attention Network, to extract high-level audio and video representations, respectively. By using weighted fusion based on a cross-attention module, the cross-modality encoder focuses on the inter-modality relations and selectively captures effective information among the audio-video modality. Specifically, bidirectional gated recurrent unit models capture long-term contextual information, explore speaker influence, and learn intra- and inter-modal interactions of the audio and video modalities in a dynamic manner. We evaluate the approach on the MELD dataset, and the experimental results show that the proposed approach achieves state-of-the-art performance on the dataset.

关键词： affective computing modality fusion attention mechanism deep learning

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the Prediction of Health Information Quality Perception Using Machine learning and Deep learning Techniques 11

The Prediction of Health Information Quality Perception Usin...

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11th international conference on Information and Communication Technology, ICoICT 2023

作者： Baqraf, Yousef Keikhosrokiani, Pantea Universiti Sains Malaysia School of Computer Sciences Penang Minden11800 Malaysia University of Oulu Faculty of Information Technology and Electrical Engineering Faculty of Medicine Oulun YliopistoPL 8000 Finland

ISBN: (纸本)9798350321982

With the substantial rise in the number of people seeking health information online, manually evaluating their perceptions of the quality of health information has become increasingly difficult. these perceptions can impact the acceptance or rejection of the information. To address this issue, the study has employed deep learning and machine learning models to automatically identify consumer perceptions of health information. Furthermore, the study used a survey to collect data from 253 individuals to train the models, measuring the perception of 18 dimensions related to health information quality. this will help health information providers to provide personalized health information that aligns with individual preferences. the RandomForest and neural network model are found to have achieved the best performance among all the algorithms with an Accuracy of over 90% in all the quality dimensions. In sum, our findings show that automating the identification of consumer perception is feasible, which is an essential step toward providing online health information that matches consumer perception and increases the willingness to use it. © 2023 IEEE.

关键词： Deep learning

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Wireless Sensors with Edge Deep learning for Detecting and Alerting the Freezing of Gait Symptoms in Parkinson's Patients 11

Wireless Sensors with Edge Deep Learning for Detecting and A...

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11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Lin, Ourong Yu, Tian Hou, Yuhan Zhu, Yi Liu, Xilin Univ Toronto Dept Elect & Comp Engn ECE Toronto ON Canada Univ Hlth Network UHN Toronto Rehabil Inst Toronto ON Canada

ISBN: (纸本)9781665462921

this paper presents the design of a wireless sensor network for detecting and alerting the freezing of gait (FoG) symptoms in patients with Parkinson's disease. A novel button pin type sensor node design was developed for easy attachment. three sensor nodes, each integrating a 3-axis accelerometer, can be placed on a patient at their ankle, thigh, and truck. Each sensor node can independently detect FoG using an on-device deep learning (DL) model, featuring a convolutional neural network (CNN). the DL model outputs from the three sensor nodes are processed in a central node using a majority voting algorithm. In a validation using a public dataset, the prototype developed achieved an FoG detection sensitivity of 88.8% and an F1 score of 85.34%, using less than 20k trainable parameters per sensor node. Once FoG is detected, an auditory signal will be generated to alert users, and the alarm signal will also be sent to mobile phones for further actions if needed. the sensor node can be easily recharged wirelessly by inductive coupling. the system is self-contained and processes all user data locally without streaming data to external devices or the cloud, thus eliminating the cybersecurity risks and power penalty associated with wireless data transmission. the developed methodology can be used in a wide range of applications.

关键词： Sensor nodes

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